Methods and systems for surface informatics based detection with machine-to-machine networks and smartphones

ABSTRACT

At a computer-enabled imaging device, a workflow including a plurality of time-stamped images is obtained. Each time-stamped image has a respective time point at which the respective time-stamped image was obtained. A first plurality of time-stamped accelerometer interval readings and a first plurality of time-stamped interval gyroscope readings are acquired, each of the readings having a respective time point at which the respective reading was acquired. A first real-time translational and rotational trajectory of the first computer-enabled imaging device is thereby obtained. Time-stamped coordinates of feature points in the first workflow are acquired at a plurality of time points during which the time-stamped images were acquired. A first dataset including the workflow, time-stamped coordinates, the real-time translational and rotational trajectory, and translational movement of the coordinates of the feature points is communicated to a data processing and display system for image processing and analysis.

TECHNICAL FIELD

This relates generally to image processing and informatics, including but not limited to surface informatics based detection using computer-enabled imaging devices.

BACKGROUND

The use of imaging technology for analyzing surface structures has a number of broad biomedical and non-biological applications, ranging from medical imaging and disease detection, to verifying the integrity of building structures. Despite significant advances in the processing and imaging capabilities of consumer devices, imaging technology and equipment enabling this surface imaging and analysis functionality has traditionally been prohibitively costly and impractical for adoption by the broad consumer demographic. Furthermore, mechanisms for aggregating subject data on a large scale for enhanced surface informatics based detection remains substantially undeveloped.

SUMMARY

Accordingly, there is a need for faster, more efficient methods, systems, and interfaces for surface informatics based detection using computer-enabled imaging devices, such as smart phones. By utilizing the robust image capture capabilities of and the multitude of sensor readings generated by basic smart phones, a variety of spatial, spectral, and/or temporal representations of datasets that include observed features for a large pool of subjects may be generated. By processing and extracting data from generated visual representations, observables changes, potential conditions, and/or pre-confirmed health conditions of a particular subject may be detected. Such methods and interfaces optionally complement or replace conventional methods for surface informatics based detection.

In accordance with some embodiments, a method is performed at a computer-enabled imaging device (e.g., a client device) having a two-dimensional pixilated detector, at least one accelerometer, at least one gyroscope, one or more processors, and memory for storing one or more programs for execution by the one or more processors. The one or more programs include programs for real-time feature detection, real-time generation of feature-based coordinate point cloud systems, and active mapping and tracking of coordinate points of a point cloud system to image features by way of implementation of the method.

The method includes obtaining a respective time-stamped image with coordinate mapped feature points for features of a subject in a plurality of subjects using the two-dimensional pixilated detector at a first frequency, thereby obtaining a workflow comprising a plurality of time-stamped images. Each time-stamped image of the workflow has a respective time point of a first plurality of time points at which the respective time-stamped image was obtained.

The method further includes acquiring a respective time-stamped accelerometer interval reading and a respective time-stamped interval gyroscope reading using the respective at least one accelerometer and the at least one gyroscope at a second frequency independent of the first frequency. In this way, a plurality of time-stamped accelerometer interval readings and a plurality of time-stamped interval gyroscope readings is acquired. Each of the time-stamped accelerometer interval readings and each of the time-stamped interval gyroscope readings have a respective time point of a second plurality of time points at which the respective reading was acquired, thereby obtaining a real-time translational and rotational trajectory of the computer-enabled imaging device which indicates a relative position of the computer-enabled imaging device with respect to the subject through the plurality of time-stamped images.

Time-stamped coordinates of the feature points in the workflow are acquired at each of the first plurality of time points, thereby obtaining real time translational movement of the coordinates of the feature points. Furthermore, a dataset is communicated through a network to the data processing and display system for image processing and analysis. The dataset includes the workflow, the time-stamped coordinates of the feature points in the workflow, the real-time translational and rotational trajectory of the computer-enabled imaging device, and the translational movement of the coordinates of the feature points in the workflow. The data processing and display system comprises one or more processors and memory for storing instructions for execution by the one or more processors, including instructions for storing the dataset in a subject data store associated with the subject in a memory location in the computer memory.

In accordance with some embodiments, a computer-enabled imaging device includes a two-dimensional pixilated detector, at least one accelerometer, at least one gyroscope, one or more processors, and memory for storing one or more programs for execution by the one or more processors. The one or more programs include programs for real-time feature detection, real-time generation of feature-based coordinate point cloud systems, and active mapping and tracking of coordinate points of a point cloud system to image features. The one or more programs include instructions for performing the operations of the client-side method described above.

In accordance with some embodiments, a computer-readable storage medium has stored therein instructions that, when executed by the computer-enabled imaging device, cause the computer-enabled imaging device to perform the operations described above.

Thus, computer-enabled imaging devices are provided with faster, more efficient methods for surface informatics based detection, thereby increasing the value, effectiveness, efficiency, and user satisfaction with such devices.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the various described embodiments, reference should be made to the Description of Embodiments below, in conjunction with the following drawings. Like reference numerals refer to corresponding parts throughout the figures and description.

FIG. 1 is a block diagram illustrating an exemplary surface informatics based detection system, in accordance with some embodiments.

FIG. 2 is a block diagram illustrating an exemplary data repository, in accordance with some embodiments.

FIG. 3 is a block diagram illustrating an exemplary client device, in accordance with some embodiments.

FIGS. 4A-4B illustrate an environment in which data is captured for a subject using one or more client devices, in accordance with some embodiments.

FIGS. 5A-5B illustrate an exemplary data structure for information obtained by client devices in a surface informatics based detection system, in accordance with some embodiments.

FIG. 6 illustrates a flowchart for the processing of subject datasets, in accordance with some embodiments.

FIGS. 7A-7J are flow diagrams illustrating a method of surface informatics based detection, in accordance with some embodiments.

DESCRIPTION OF EMBODIMENTS

Reference will now be made to embodiments, examples of which are illustrated in the accompanying drawings. In the following description, numerous specific details are set forth in order to provide an understanding of the various described embodiments. However, it will be apparent to one of ordinary skill in the art that the various described embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

It will also be understood that, although the terms first, second, etc. are, in some instances, used herein to describe various elements, these elements should not be limited by these terms. These terms are used only to distinguish one element from another. For example, a first smart phone could be termed a second smart phone, and, similarly, a second smart phone could be termed a first smart phone, without departing from the scope of the various described embodiments. The first smart phone and the second smart phone are both smart phones, but they are not the same smart phone.

The terminology used in the description of the various embodiments described herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various described embodiments and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

As used herein, the term “if” is, optionally, construed to mean “when” or “upon” or “in response to determining” or “in response to detecting” or “in accordance with a determination that,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” is, optionally, construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event]” or “in accordance with a determination that [a stated condition or event] is detected,” depending on the context.

As used herein, the term “exemplary” is used in the sense of “serving as an example, instance, or illustration” and not in the sense of “representing the best of its kind.”

FIG. 1 is a block diagram illustrating an exemplary surface informatics based detection system 100, in accordance with some embodiments. The detection system 100 includes a number of client devices (also called “client systems,” “client computers,” or “clients”) 104-1, 104-2, . . . 104-n communicably connected to a data repository 108 by one or more networks 106 (e.g., the Internet, cellular telephone networks, mobile data networks, other wide area networks, local area networks, metropolitan area networks, and so on). In some embodiments, the one or more networks 106 include a public communication network (e.g., the Internet and/or a cellular data network), a private communications network (e.g., a private LAN or leased lines), or a combination of such communication networks. In some embodiments, the one or more networks 106 use the HyperText Transport Protocol (HTTP) and the Transmission Control Protocol/Internet Protocol (TCP/IP) to transmit information between devices or systems. HTTP permits client devices to access various resources available via the one or more networks 106. The various embodiments of the invention, however, are not limited to the use of any particular protocol.

In some embodiments, the client devices 104-1, 104-2, . . . 104-n are computing devices such as cameras, video recording devices, smart watches, personal digital assistants, portable media players, smart phones, tablet computers, 2D devices, 3D (e.g., virtual reality) devices, laptop computers, desktop computers, televisions with one or more processors embedded therein or coupled thereto, in-vehicle information systems (e.g., an in-car computer system that provides navigation, entertainment, and/or other information), and/or other appropriate computing devices that can be used to communicate with other client devices 104 and/or the data repository 108. In some embodiments, the data repository 108 is a single computing device such as a computer server, while in other embodiments, the data repository 108 is implemented by multiple computing devices working together to perform the actions of a server system (e.g., cloud computing).

Users 102-1, 102-2, . . . 102-n employ the client devices 104-1, 104-2, . . . 104-n to obtain or generate data for transmission to the data repository 108 and/or other client devices, or to receive, display, and/or manipulate data (e.g., data generated, obtained, or produced on the device itself, data received from the data repository 108 or other client devices, etc.). In some embodiments, the client devices 104 capture multimedia data (e.g., time-stamped images, video, audio, etc.) and acquire associated meta data (e.g., environmental information (time, geographic location, etc.), device readings, such as sensor readings from accelerometers, gyroscopes, etc.) for communication to the data repository 108 for further processing and analysis. The same or other client devices 104 may subsequently receive data from the data repository 108 and/or other client devices for display (e.g., constructed two or three-dimensional maps, point clouds, textured maps, etc.). In some embodiments, separate client devices 104 (e.g., client device 104-4, a dedicated display terminal used by physicians) are configured for viewing received data and capturing/acquiring multimedia data and meta data.

In some embodiments, data is sent to and viewed by the client devices in a variety of output formats, and/or for further processing or manipulation (e.g., CAD programs, 3D printing, virtual reality displays, holography applications, etc.). In some embodiments, data is sent for display to the same client device that performs the image capture and acquires sensor readings (e.g., client devices 104), and/or other systems and devices (e.g., data apparatus 108, a client device 104-4 that is a dedicated viewing terminal, etc.). In some embodiments, client devices 104 access data and/or services provided by the data repository 108 by execution of various applications. For example, in some embodiments client devices 104 execute web browser applications that can be used to access services provided by the data repository 108. As another example, one or more of the client devices 104-1, 104-2, . . . 104-n execute software applications that are specific to viewing and manipulating data (e.g., surface informatics “apps” running on smart phones or tablets).

In some embodiments, client devices 104 are used as control devices for synchronizing operational processes of one or more client devices 104. For instance, in some embodiments, one or more client devices 104 are used to dynamically generate control commands for transmission to other client devices for synchronized data capture (e.g., synchronous image/meta data capture with respect to temporal, spatial, or spectral parameters). As an example, one or more client devices 104 generate control commands for time-synchronized image capture of a particular subject using multiple client devices 104 across a predefined period of time (e.g., multiple client devices 104 having different positions or orientations with respect to a subject capturing a workflow of images at the same frequency), or at specified periods of time (e.g., each of multiple client devices 104 capturing a stream of 100 images of the same subject each day for a week). In some embodiments, control commands are also be synchronized by spatial parameters of the client devices 104 with respect to a subject, an environment, or one another (e.g., image capture synchronized such that images are captured from known positions and orientations with reference to a subject). Moreover, in some embodiments control commands are synchronized with respect to spectral aspects of a subject or environment (e.g., identifying a common feature among images captured by different client devices 104, and synchronizing image capture based on the identified feature). In some embodiments, control commands are transmitted by a client device 104 to other client devices such that the client device from which the control commands are originally transmitted initiate a process (e.g., image capture sequence using multiple client devices) and terminate the process with respect to its own operations, while the other client devices continue through to completion of the process. In some embodiments, control commands are generated by the data repository 108 and transmitted to the client devices 104 for execution of a synchronized process.

Users interacting with the client devices 104-1, 104-2, . . . 104-n can participate in or contribute to services provided, or processing performed, by the data repository 108 by submitting datasets (or select portions thereof), where the submitted datasets include captured multimedia data (e.g., time-stamped images, video, audio, etc.) and/or acquired meta data (e.g., associated sensor readings). In some embodiments, information is posted on a user's behalf by systems and/or services external to the data repository 108 (e.g., by a user's physician). In some embodiments, user submitted datasets are retrieved and processed by the data repository 108, the results of which are compared with one another or analyzed in order to detect temporal observable changes (e.g., biological or non-biological), potential conditions, and/or pre-confirmed health conditions (as described in greater detail below with respect to FIGS. 7F-7J).

The data repository 108 stores, processes, and/or analyzes data received from one or more client devices 104 (e.g., datasets which include multimedia data, associated meta data, localization data, etc.). The resulting data of such processing and analysis are in turn disseminated to the same and/or other client devices for viewing, manipulation, and/or further processing and analysis. In some embodiments, the data repository 108 consolidates data received from one or more client devices 104 and performs one or more geomatics based processes. For example, using associated meta data and localization data, the data repository 108 constructs two or three-dimensional maps (e.g., by matching two-dimensional features identified across an image workflow, estimating parallax between images, and adding points to a map when a parallax threshold is satisfied), where the constructed maps are used to create dense point clouds and/or generate textured meshes representing a subject. In some embodiments, useful biological or non-biological data is further derived and extracted from visual representations generated by geomatics based processes (e.g., extracting data from the spatial, spectral, and/or temporal representations of subject datasets, such as generated maps, point clouds, and/or meshes). Extracted data can be further processed or analyzed for detection purposes (e.g., correlating feature/temporal data of a subject to feature/temporal data of other subjects to detect a temporally observable change or pre-confirmed condition). Furthermore, in some embodiments, data analyses or detection outcomes are disseminated to one or more devices (e.g., client devices 104), server systems (e.g., data repository 108), and/or devices of associated individuals (e.g., devices of physicians).

FIG. 2 is a block diagram illustrating an exemplary data repository 108, in accordance with some embodiments. In some embodiments, the data repository 108 is a data repository apparatus, server system, or any other electronic device for receiving, collecting, storing, displaying, and/or processing data received from a plurality of devices over a network (sometimes referred to alternatively as a data processing and display system).

The data repository 108 typically includes one or more processing units (processors or cores) 202, one or more network or other communications interfaces 204, memory 206, and one or more communication buses 208 for interconnecting these components. The communication buses 208 optionally include circuitry (sometimes called a chipset) that interconnects and controls communications between system components. The data repository 108 optionally includes a user interface (not shown). The user interface, if provided, may include a display device and optionally includes inputs such as a keyboard, mouse, trackpad, and/or input buttons. Alternatively or in addition, the display device includes a touch-sensitive surface, in which case the display is a touch-sensitive display.

Memory 206 includes high-speed random-access memory, such as DRAM, SRAM, DDR RAM, or other random-access solid-state memory devices; and may include non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, and/or other non-volatile solid-state storage devices. Memory 206 optionally includes one or more storage devices remotely located from the processor(s) 202. Memory 206, or alternately the non-volatile memory device(s) within memory 206, includes a non-transitory computer-readable storage medium. In some embodiments, memory 206 or the computer-readable storage medium of memory 206 stores the following programs, modules and data structures, or a subset or superset thereof:

-   -   an operating system 210 that includes procedures for handling         various basic system services and for performing hardware         dependent tasks;     -   a network communication module 212 that is used for connecting         the data repository 108 to other computers, systems, and/or         client devices 104 via the one or more communication network         interfaces 204 (wired or wireless) and one or more communication         networks (e.g., the one or more networks 106)     -   a subject data store 214 for storing data associated with one or         more subjects (e.g., received from one or more associated client         devices 104, FIGS. 1 and 3), such as:         -   subject information 2140 for storing additional or             supplemental information for one or more subjects (e.g.,             user medical history, biological data, personal profiles,             and/or any additional info helpful for rendering diagnosis);         -   multimedia data 2141 for storing multimedia data (e.g.,             time-stamped images, video, audio, etc.) captured by one or             more sensors or devices (e.g., two-dimensional pixilated             detector and/or microphone of a client device 104, FIG. 3);         -   localization data 2142 for environmental device             measurements, such as a focal length, sensor frequencies             (e.g., the respective frequency at which sensors of the             client device captured data, such as an accelerometer             frequency, a gyroscope frequency, a barometer frequency,             etc.), accelerometer readings (e.g., in meters/sec²),             translational data (e.g., (x, y, z) coordinates of the             client device with respect to a pre-defined axes or point of             reference), rotational data (e.g., roll (φ), pitch (θ), yaw             (ψ)), and/or any additional sensor or device measurements or             readings for determining spatial, spectral, and/or temporal             characteristics of a client device or subject;         -   meta data 2143 for storing device data or data associated             with captured multimedia, such as a device identifier (e.g.,             identifying the device of a group of devices that captured             the multimedia item, which may include an arbitrary             identifier, a MAC address, a device serial number, etc.),             temporal meta data (e.g., date and time of a corresponding             capture), location data (e.g., GPS coordinates of the             location at which multimedia item was captured), a             multimedia capture frequency (e.g., the frequency at which a             stream of images is captured), device configuration settings             (e.g., image resolution captured multimedia items, frequency             ranges that the pixilated detector of a client device 104 is             configured to detect), and/or other camera data or             environmental factors associated with captured multimedia;         -   feature data 2144 for storing quantitative and/or             qualitative data for observations of a class of features             (e.g., feature data for an observation corresponding to             observed lesions may include data related to a location of             observed lesions, such as diffuse or localized, lesion size             and size distribution, percent body surface area, etc.);             and/or         -   temporal data 2145 for storing data representing observed             changes in values of feature data over time (e.g.,             percentage increase in number of observed lesions);     -   geomatics module 216 for processing, manipulating, and analyzing         datasets (e.g., received from one or more client devices 104) in         order to generate and view spatial, spectral, and/or temporal         representations of subject datasets, which includes:         -   map generator 2160 for constructing two or three-dimensional             maps from one or more datasets (e.g., corresponding to one             or more capture sessions, and received from one or more             client devices);         -   point cloud generator 2161 for creating dense point clouds             (e.g., consisting of tens of thousands of points) from             constructed two or three-dimensional maps; and/or         -   mesh generator 2162 for generating meshes representing a             subject by processing the created dense point cloud (e.g.,             using a surface reconstruction algorithm), and for adding             texture to the meshes to generate texture-mapped meshes             (e.g., by applying texture mapping algorithms);     -   a processing module 218 for processing, analyzing, and         extracting data (e.g., biological/non-biological feature data         and/or temporal data) from generated spatial, spectral, and/or         temporal representations of subject datasets (e.g., constructed         maps, dense point clouds, meshes, texture-mapped meshes, etc.),         and for detecting temporal observable changes and/or conditions         (e.g., potential conditions, health conditions, etc.) (e.g., by         correlating data, calculating numerical scores, determining         satisfaction of score thresholds, etc.); and     -   dissemination module 220 for updating subject data stores (e.g.,         214), sending alerts (e.g., to remote devices associated with a         subject), and/or providing notifications (e.g., to caretakers         associated with human subjects).

The subject data store 214 (and any other data storage modules) stores data associated with one or more subjects in one or more types of databases, such as graph, dimensional, flat, hierarchical, network, object-oriented, relational, and/or XML databases, or other data storage constructs.

FIG. 3 is a block diagram illustrating an exemplary client device 104, in accordance with some embodiments. The client device 104 typically includes one or more processing units (processors or cores) 302, one or more network or other communications interfaces 304, memory 306, and one or more communication buses 308 for interconnecting these components. The communication buses 308 optionally include circuitry (sometimes called a chipset) that interconnects and controls communications between system components. The client device 104 includes a user interface 310. The user interface 310 typically includes a display device 312. In some embodiments, the client device 104 includes inputs such as a keyboard, mouse, and/or other input buttons 316. Alternatively or in addition, in some embodiments, the display device 312 includes a touch-sensitive surface 314, in which case the display device 312 is a touch-sensitive display. In client devices that have a touch-sensitive display 312, a physical keyboard is optional (e.g., a soft keyboard may be displayed when keyboard entry is needed). The user interface 310 also includes an audio output device 318, such as speakers or an audio output connection connected to speakers, earphones, or headphones. Furthermore, some client devices 104 use a microphone and voice recognition to supplement or replace the keyboard. Optionally, the client device 104 includes an audio input device 320 (e.g., a microphone) to capture audio (e.g., speech from a user). Optionally, the client device 104 includes a location detection device 322, such as a GPS (global positioning satellite) or other geo-location receiver, for determining the location of the client device 104.

The client device 104 also optionally includes an image/video capture device 324, such as a camera or webcam. In some embodiments, the image/video capture device 324 includes a two-dimensional pixilated detector/image sensor configured to capture images at one or more predefined resolutions (e.g., a low resolution, such as 480×360, and a high resolution, such as 3264×2448). In some embodiments, the image/video capture device 324 captures a workflow of images (e.g., a stream of multiple images) at a predefined frequency (e.g., 30 Hz). In some embodiments, the client device 104 includes a plurality of image/video capture devices 324 (e.g., a front facing camera and a back facing camera), where in some implementations, each of the multiple image/video capture devices 324 captures a distinct workflow for subsequent processing (e.g., capturing images at different resolutions, ranges of light, etc.). Optionally, the client device 104 includes one or more illuminators (e.g., a light emitting diode) configured to illuminate a subject or environment. In some embodiments, the one or more illuminators are configured to illuminate specific wavelengths of light.

In some embodiments, the client device 104 includes one or more sensors 326 including, but not limited to, accelerometers, gyroscopes, compasses, magnetometer, light sensors, near field communication transceivers, barometers, humidity sensors, temperature sensors, proximity sensors, and/or other sensors/devices for sensing and measuring various environmental conditions. In some embodiments, the one or more sensors operate and obtain measurements at respective predefined frequencies.

Memory 306 includes high-speed random-access memory, such as DRAM, SRAM, DDR RAM or other random-access solid-state memory devices; and may include non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. Memory 306 may optionally include one or more storage devices remotely located from the processor(s) 302. Memory 306, or alternately the non-volatile memory device(s) within memory 306, includes a non-transitory computer-readable storage medium. In some embodiments, memory 306 or the computer-readable storage medium of memory 306 stores the following programs, modules and data structures, or a subset or superset thereof:

-   -   an operating system 328 that includes procedures for handling         various basic system services and for performing hardware         dependent tasks;     -   a network communication module 330 that is used for connecting         the client device 104 to other computers, systems (e.g., data         repository 108), and/or client devices 104 via the one or more         communication network interfaces 304 (wired or wireless) and one         or more communication networks, such as the Internet, cellular         telephone networks, mobile data networks, other wide area         networks, local area networks, metropolitan area networks, and         so on;     -   an image/video capture module 332 (e.g., a camera module) for         processing a respective image or video captured by the         image/video capture device 324, where the respective image or         video may be sent or streamed (e.g., by a client application         module 336) to the data repository 108;     -   an audio input module 334 (e.g., a microphone module) for         processing audio captured by the audio input device 320, where         the respective audio may be sent or streamed (e.g., by a client         application module 340) to the data repository 108;     -   a location detection module 336 (e.g., a GPS, Wi-Fi, or hybrid         positioning module) for determining the location of the client         device 104 (e.g., using the location detection device 322) and         providing this location information for use in various         applications (e.g., client application module 340);     -   a sensor module 338 for acquiring, processing, and transmitting         environmental device measurements, such as a focal length,         sensor frequencies (e.g., accelerometer frequency, a gyroscope         frequency, etc.), accelerometer readings (e.g., in meters/sec²),         translational data (e.g., (x, y, z) coordinates of the client         device with respect to a pre-defined axes or point of         reference), rotational data (e.g., roll (φ), pitch (θ), yaw         (ψ)), and/or any additional sensor or device measurements or         readings for determining spatial, spectral, and/or temporal         characteristics of the client device or subject; and     -   one or more client application modules 340, including the         following modules (or sets of instructions), or a subset or         superset thereof:         -   a web browser module (e.g., Internet Explorer by Microsoft,             Firefox by Mozilla, Safari by Apple, or Chrome by Google)             for accessing, viewing, and interacting with web sites             (e.g., a social-networking web site provided by the data             repository 108), subject datasets (e.g., including             time-stamped images of a subject, real-time translational             and rotational trajectory of the client device, etc.),             and/or spatial, spectral, and/or temporal representations of             subject datasets (e.g., constructed maps, dense point             clouds, meshes, texture-mapped meshes, etc.); and/or         -   other optional client application modules for viewing and/or             manipulating datasets of generated representations, such as             applications for photo management, video management, a             digital video player, computer-aided design (CAD), 3D             viewing (e.g., virtual reality), 3D printing, holography,             and/or other graphics-based applications.

Each of the above identified modules and applications correspond to a set of executable instructions for performing one or more functions as described above and/or in the methods described in this application (e.g., the computer-implemented methods and other information processing methods described herein). These modules (i.e., sets of instructions) need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules are, optionally, combined or otherwise re-arranged in various embodiments. In some embodiments, memory 206 and/or 306 store a subset of the modules and data structures identified above. Furthermore, memory 206 and/or 306 optionally store additional modules and data structures not described above.

Furthermore, in some implementations, the functions of any of the devices and systems described herein (e.g., client devices 104, data repository 108) are interchangeable with one another and may be performed by any other devices or systems, where the corresponding sub-modules of these functions may additionally and/or alternatively be located within and executed by any of the devices and systems. As one example, although the data repository 108 (FIG. 2) includes modules for processing subject datasets (e.g., geomatics module 216) and extracting data from generated spatial, spectral, and/or temporal representations of subject datasets (e.g., processing module 218), in some embodiments the client device 104 may include analogous modules and device capabilities for performing the same operations (e.g., processing is additionally and/or alternatively performed by the same client device used for image capture and sensor acquisitions). The devices and systems shown in and described with respect to FIGS. 1 through 3 are merely illustrative, and different configurations of the modules for implementing the functions described herein are possible in various implementations.

FIGS. 4A-4B illustrate an environment in which data is captured for a subject using one or more client devices 104, in accordance with some embodiments.

Specifically, the environment shown in FIG. 4A includes a single client device 104 for capturing images of a subject (e.g., user 102-1) and for acquiring various sensor readings. Although the client device 104 is a smart phone in the example illustrated, in other implementations the client device 104 may be any electronic device with image capture capabilities (e.g., a camera, a PDA, etc.). Furthermore, while the subject is a live, biological subject (e.g., a human), other non-biological applications exist (as described in greater detail with respect to FIGS. 7A-7J).

In some implementations, the client device 104 is used to capture one or more still-frame images, video sequences, and/or audio recordings of the subject from one or more positions and angles. Concurrently with image capture, the client device 104 also acquires multiple time-stamped sensor readings of various environmental conditions, such as a measured acceleration and orientation of the client device 104 as the client device 104 is re-positioned and oriented into new poses to capture additional images. By using the captured images, acquired sensor readings, and additional device/image meta data (e.g., timestamps, resolution, image capture/sensor frequencies, focal length, etc.), multi-dimensional maps and point clouds of the subject are constructed in some embodiments. Values for various observed features (e.g., characteristics of a visible skin lesion) are then be extracted from the constructed maps, point clouds, and/or meshes, which subsequently are analyzed spatially, spectrally, and/or temporally to detect temporal observable changes or health conditions of the subject. The detection-based method is described in greater detail with respect to the method 7000 in FIGS. 7A-7J.

FIG. 4A illustrates a set of predefined axes which provide relative localization information of the client device 104 and subject. Localization information may include a respective trajectory of the client device 104 or subject with respect to the predefined axes, which includes translational data (e.g., (x, y, z) coordinates) and/or rotational data (e.g., roll (φ), pitch (θ), yaw (ψ)). More specifically, in the example shown, values for the rotational trajectory of the client device 104 are defined as an angle of rotation within the x-y axis (i.e., yaw (ψ)), an angle of rotation within the y-z axis (i.e., pitch (θ)), and an angle of rotation within the x-z axis (i.e., roll (φ).

As the client device 104 captures multiple images, the images form a workflow in which each image of the workflow has a respective timestamp, and the measured trajectory of the client device 104 continually changes. Changes in trajectory may, for example, be derived from one or more time-stamped sensor readings by the client device 104 (e.g., sensors 326, such as an accelerometer and/or a gyroscope). In particular, in some embodiments, as the position of the client device 104 changes as new images are captured, the translational and and/or rotational trajectory of the client device 104 is derived for any corresponding time-stamped image, and at any given time point, in real-time using accelerometer and gyroscope readings.

The derived translational and rotational trajectory of the client device 104, in combination with the workflow of captured images, forms a respective dataset that is stored in a subject data store (e.g., subject data store 214, FIG. 2). Using the dataset, multi-dimensional maps may be constructed, from which a dense point cloud consisting of multiple points (e.g., on the order of tens of thousands) representing the subject can be created. Optionally, surface reconstruction algorithms may then be applied to generate representative textured polygonal meshes of the subject.

From the generated visual representations, data may be extracted for observations sets corresponding to various classes of features. For example, for an observation set corresponding to a class that includes skin features, an observation may include data values corresponding to different characteristics of observed moles, such as pigmentation. By comparing the extracted data against stored data, or by analyzing the extracted data itself, a temporal change in the data, potential conditions, or identifiable health conditions may detected.

The construction of multi-dimensional maps, the creation of point clouds, the generation of meshes, and their use, is described in greater detail with respect to FIGS. 7A-7J.

FIG. 4B illustrates an environment in which a plurality of distinct client devices 104 is used for capturing images of a subject and for acquiring various sensor readings.

FIG. 4B illustrates an example in which four smart phones are positioned at different angles and orientations with respect to each other and the subject (in some embodiments, each of the client devices 104-2 through 104-4 represent the client device 104-1 at different positions and different times). As described in greater detail below, the use of multiple client devices 104 is advantageous for obtaining and acquiring comprehensive datasets, and ultimately for enabling an enhanced analytical approach to processing subject data.

As in many data processing applications, the availability of additional data points allows for an enhanced and more detailed generation of spatial, spectral, and/or temporal representations of a subject and observed features. Moreover, additional client devices 104 may be used to acquire data concurrently with the data capture session of the first client device 104-1, so as to acquire time-stamped images and sensor readings from a variety of viewpoints and angles. Alternatively, additional client devices 104 may be used to acquire data at different times from the data capture session of the first client device 104-1 in order to assemble a temporal stack of spatial and spectral data.

Multiple client devices 104 may also be used to capture images of the first subject at different resolutions (e.g., a first dataset for capturing low-resolution images, a second dataset for capturing high-resolution images, etc.), and/or to capture image workflows representing distinct frequencies of light (or ranges of frequencies) (e.g., a first client device 104 configured to detect visible light frequencies, a second client device 104 configured to detect IR light frequencies).

FIGS. 5A-5B illustrate an exemplary data structure for data obtained by client devices in a surface informatics based detection system 100, in accordance with some embodiments.

In particular, the exemplary data structures illustrated in FIG. 5A correspond to data obtained and acquired by one or more components or sensors of the client devices (e.g., time-stamped images of a subject using a pixilated detector, associated meta data, time-stamped accelerometer and gyroscope readings, etc.), while the exemplary data structures illustrated in FIG. 5B correspond to data extracted and derived from spatial, spectral, and/or temporal representations and subject datasets (e.g., feature data and temporal data extracted from constructed maps, point clouds, meshes, etc.). In some embodiments, data structures include additional or fewer types of data (e.g., data acquired from optional sensors of the client device 104) or parameters associated with data acquisition using the client devices. In some embodiments, all or portions of the illustrated data structures are stored in the memory of the client devices (e.g., memory 306, FIG. 3) and/or server systems (e.g., subject data store 214, geomatics data store 230, FIG. 2). Furthermore, in some embodiments, all or portions of the illustrated data structures are transmitted by one or more client devices 104 to the data repository 108 for further processing.

FIG. 5A illustrates an exemplary data structure within which data acquired by client devices 104 is populated. For a process involving the capture of multimedia data (e.g., using a pixilated camera of the client device 104), the data structure may include a workflow identifier (e.g., for identifying a particular workflow to which one or more images/multimedia items correspond), an image identifier (e.g., for identifying a particular image/multimedia item of a workflow), and/or a multimedia capture resolution (e.g., the resolution of a captured image or video, the bit rate of captured audio). The data structure may also include associated meta data of the captured multimedia, such as a device identifier (e.g., identifying the device of a group of devices that captured the multimedia item, which may include an arbitrary identifier, a MAC address, a device serial number, an RFID, etc.), temporal meta data (e.g., date and time of a corresponding capture), a location (e.g., GPS coordinates of the location at which multimedia item was captured), a multimedia capture frequency (e.g., the frequency at which a stream of images is captured), device configuration settings (e.g., frequency ranges that the pixilated detector of the client device is configured to detect), and/or other camera data or environmental factors associated with the captured multimedia.

FIG. 5A additionally illustrates an exemplary data structure populated with acquired localization data. As shown, for a corresponding device and a time of data capture (and/or other data or meta data that may be utilized for synchronization), localization data may include a focal length of the device, sensor frequencies (e.g., the respective frequency at which sensors of the client device captured data, such as an accelerometer frequency, a gyroscope frequency, a barometer frequency, etc.), an accelerometer reading (e.g., in meters/sec²), translational data (e.g., (x, y, z) coordinates of the client device with respect to a pre-defined axes or point of reference), rotational data (e.g., roll (φ), pitch (θ), yaw (ψ)), and/or any additional sensor or device measurements or readings for determining spatial, spectral, and/or temporal characteristics of the client device or captured subject.

Referring now to FIG. 5B, the exemplary data structure includes data derived and extracted from processed user submitted data. For example, user submitted datasets that include multimedia data, associated meta data, and localization data, may be processed to construct multidimensional maps and to create dense point clouds representing a captured subject. Point clouds may be further processed using surface reconstruction algorithms and texture mapped. From the generated maps, point clouds, and/or meshes, useful biological/non-biological data may be extracted for further detection applications or analysis. As shown, such data may include a corresponding class of biological/non-biological features for different surface informatics based detection and analysis scenarios (e.g., genetic features, aging features, disease specific features), an observation of a corresponding class of features (e.g., for genetic features, observations of the eye, nose, and mouth), values for different aspects of the corresponding biological/non-biological features (e.g., for observations of the eye, a value for observed eye color, a value for observed eye shape, a value for an observed eye size), temporal data (e.g., indicating a change in corresponding values of an observation over time, such as a change in the size of a subject's eye since a previous measurement), and/or additional spatial, spectral, and/or temporal data that may be derived.

FIG. 6 illustrates a flowchart for the processing of subject datasets, in accordance with some embodiments.

As shown, in some embodiments, a plurality of time-stamped images (e.g., low resolution two-dimensional images) is obtained at a respective frequency (602), and a plurality of sensor readings of the device (e.g., time-stamped accelerometer readings and time-stamped gyroscope readings) is acquired at a respective frequency (604).

In some embodiments, a process for concurrent localization and mapping (606) is performed such that a real-time translational and rotational trajectory of a respective acquiring device (e.g., (x, y, z) translational data, roll (φ), pitch (θ), yaw (ψ) rotational data) is obtained (608) from the acquired plurality of sensor readings. Furthermore, in some embodiments, a multidimensional map is constructed (610) from the obtained and acquired data (e.g., a dataset), which includes the time-stamped images and real-time translational and rotational trajectory of the acquiring device, in addition to optional data such as the time-stamped coordinates of features identified across the time-stamped images, and the translational movement of those coordinates across the time-stamped images. In some embodiments, the constructed map includes a relatively sparse number of points (e.g., hundreds of points).

The multidimensional map is further used in conjunction with additional images (e.g., high-resolution images) captured (612) by the one or more client devices to create (614) (e.g., multi-view stereo) a dense point cloud. In comparison to the constructed map, the resulting dense point cloud (616) includes many points (e.g., tens of thousands). In some embodiments, each of the two or three-dimensional points of the dense point cloud represents an average value of a respective single pixel (or group of pixels) across the time-stamped images, and includes a surface normal computed from translational and rotational values of the time-stamped images.

The processing of subject datasets (e.g., datasets which include workflows and translational and rotational trajectory information) shown in FIG. 6 is described in greater detail below with respect to FIGS. 7A-7J.

FIGS. 7A-7J are flow diagrams illustrating a method 7000 of surface informatics based detection, in accordance with some embodiments. In some implementations, the method 7000 is performed by one or more electronic devices of one or more systems (e.g., client devices 104, FIGS. 1 and 3), a server system (e.g., data repository 108, FIGS. 1 and 2), and/or additional devices/systems/entities. Thus, in some implementations, the operations of the method 7000 described herein are entirely interchangeable, and respective operations of the method 7000 are performed by any one of the aforementioned devices and systems, or combination of devices and systems (e.g., the operations described in FIGS. 7C-7J may be performed by the client device). For ease of reference, the methods herein will be described as being performed by a first smart phone (e.g., client device 104) and a data repository apparatus (e.g., data repository 108, alternatively referred to as a data processing and display system), both of which compose the machine-to-machine network (e.g., surface informatics based detection system 100, FIG. 1). While parts of the method 7000 are described with respect to a first smart phone, any operations or combination of operations of the method 7000 may be performed by any client device 104 having image capture capabilities (e.g., a camera device, a computer-enabled imaging device, a PDA, etc.).

In the example provided, FIGS. 7A-7B correspond to instructions/programs stored in a memory (e.g., memory 306, FIG. 3) or other computer-readable storage medium of the first smart phone (e.g., memory 306 of client device 104, FIG. 3). The first smart phone has one or more first processors (e.g., 302) for executing the stored instructions/programs, at least one first accelerometer (e.g., sensor 326), at least one first gyroscope (e.g., sensor 326), and a first two-dimensional pixilated detector (e.g., image/video capture module 332). Furthermore, FIGS. 7C-7J correspond to instructions/programs stored in a memory or other computer-readable storage medium of the data repository apparatus (e.g., memory 206 of data repository 108, FIG. 2), which has one or more processors for executing the stored instructions/programs. Instructions/programs stored in the memory of the client device 104 and/or the data repository apparatus 108 may include programs for real-time feature detection, real-time generation of feature-based coordinate point cloud systems, and/or active mapping and tracking of coordinate points of a point cloud system to image features (e.g., geomatics module 224, processing module 232, FIG. 2). Optionally, the first smart phone includes one or more additional sensors (e.g., barometer, compass, light sensors, etc.) for acquiring additional sensor readings that may be used as additional mathematical variables in spatial, spectral, and/or temporal processing operations (as described in greater detail below).

The first smart phone obtains (7002) a respective time-stamped image of a first subject in a plurality of subjects using a first two-dimensional pixilated detector of a first smart phone at a first frequency, thereby obtaining a first workflow. The first workflow includes a first plurality of time-stamped images. In some embodiments, the time-stamped images have coordinate mapped feature points for features of the first subject. That is, in some embodiments, observable features of the first subject (e.g., a human subject's eyes) comprise a plurality of feature points that can be mapped to a predefined coordinate system (e.g., two or three-dimensional). Furthermore, in some embodiments, each time-stamped image of the first workflow has a respective time point of a first plurality of time points at which the respective time-stamped image was obtained.

In some embodiments, at least some of the time-stamped images of the first workflow obtained at the first frequency are images having a first resolution (e.g., a low resolution, such as 480×360) that enables real-time image capture at the first frequency. In some embodiments, the first resolution is greater than 1000×1000, while in other embodiments, the first resolution is less than 1000×1000. In some embodiments, at least some of the time-stamped images of the first workflow are images having a second resolution distinct from the first resolution (e.g., a high resolution, such as 3264×2448). In some embodiments, a resolution of each image in a first subset of the first workflow is (7004) less than 1000×1000 (e.g., low-resolution images so as to enable real-time vision processing). In some embodiments, a resolution of each image in a second subset of the first workflow is (7006) greater than 1000×1000.

In some embodiments, the first subject is illuminated by natural light while obtaining the first workflow. In some embodiments, the first subject is illuminated by artificial light while obtaining the first workflow. In some embodiments, the first smart phone illuminates the first subject with a light emitting diode of the first smart phone while obtaining the first workflow. In some embodiments, the first smart phone illuminates the first subject with polarized light while obtaining the first workflow. In some embodiments, the first smart phone illuminates the first subject with specific wavelengths of light while obtaining the first workflow (e.g., IR, UV). In some embodiments, the first smart phone illuminates the first subject while obtaining the first workflow, and reflected light returning from the first subject is filtered through a polarizer. In some embodiments, the first smart phone illuminates the first subject while obtaining the first workflow, and reflected light returning from the first subject is filtered such that the first two-dimensional pixilated detector is exposed to a specific wavelength range of light that is more than the wavelength range of the illuminated light.

In some embodiments, the first smart phone is configured to image subjects with a predefined surface type, color, brightness, size, and shape characteristics (e.g., by adjusting one or more device or software settings for the first smart phone).

The first smart phone acquires (7008) a respective time-stamped accelerometer interval reading and a respective time-stamped gyroscope interval reading using the respective at least one first accelerometer and the at least one first gyroscope of the first smart phone at a second frequency independent of the first frequency. A first plurality of time-stamped accelerometer interval readings and a first plurality of time-stamped interval gyroscope readings is thereby obtained. Furthermore, in some embodiments, each of the time-stamped accelerometer interval readings and each of the time-stamped interval gyroscope readings have a respective time point of a second plurality of time points at which the respective reading was acquired. As a result, a first real-time translational and rotational trajectory of the first smart phone is thereby obtained which indicates a relative position of first smart phone with respect to the first subject through the first plurality of time-stamped images. The accelerometer and gyroscope readings change, for example, as the position of the first smart phone changes during image capture. Thus, based on a predefined or known initial trajectory of the first smart phone, and using the acquired accelerometer readings, gyroscope readings, and the time at which the readings were acquired, the first real-time translational and rotational trajectory of the first smart phone may be obtained, where the obtained trajectory represents a change in the position and orientation of the first smart phone (e.g., new poses of the first smart phone relative to the subject). An exemplary data structure including translational and rotational trajectory data of the first smart phone is shown in FIGS. 5A-5B.

In some embodiments, translational values for the translational trajectory include (7010) (x, y, z) translational values, and rotational values for the rotational trajectory include (yaw, pitch, roll) rotational values (the combination of (x, y, z) translational values, (yaw, pitch, roll) rotational values, and the focal length at a given point in time for a respective smart phone is referred to as “camera pose.”). An exemplary predefined set of axes for defining the translational and rotational trajectory is illustrated in FIG. 4A. In some embodiments, the first frequency and second frequency are (7012) are each independently between 10 Hz and 100 Hz. In some embodiments, the first frequency is (7014) 30 Hz and the second frequency is 100 Hz. In some embodiments, the first frequency and/or second frequency are less than 10 Hz, or more than 100 Hz.

In some embodiments, the first plurality of time points (while obtaining the time-stamped images, 7002) and second plurality of time points (while acquiring the accelerometer and gyroscope interval readings, 7008) are within a first timeframe corresponding to a first capture session. That is, in some embodiments, the time-stamped images are obtained and the interval readings are acquired concurrently during the same capture session (e.g., during the same period of time on the same day).

In some embodiments, the first smart phone acquires time-stamped coordinates of the feature points in the first workflow at each of the first plurality of time points, thereby obtaining real time translational movement of the coordinates of the feature points. In each time-stamped image of the first workflow, statistics of pixel intensities in a local neighborhood around each pixel are computed, where the computing is iterated over every pixel location (or alternatively a subset of pixel locations). In doing so, groups of neighboring pixels with significant variation along two spatial dimensions are detected and identified. Subsequently, thresholds (e.g., of pixel intensity variation) are defined, and every pixel location that exceeds one or more predefined thresholds corresponds to a positive feature detection. Furthermore, in some embodiments, for every feature detected, statistics are computed for the differences between neighboring pixels surrounding the pixel location. These computed statistics act as a fingerprint (i.e., a descriptor that uniquely identifies that feature). As described in greater detail below, for any two successive images, such as a first and second time-stamped image, features are detected in each image, and descriptors for every detected feature are computed. For each descriptor in the second time-stamped image, a search is performed for the most similar descriptor in the first time-stamped image until a set is obtained for potentially matched features whose locations are known in both the first and second time-stamped image. For geometric consistency, in some embodiments, one or more algorithms (e.g., Random Sample Consensus (RANSAC)) are applied to filter out feature matches that are not geometrically consistent with other feature matches, resulting in a set of reliably tracked two-dimensional feature points between the first and second time-stamped image. Repeating the above process in real time over every overlapping pair of frames leads to a set of two-dimensional features whose translations are known.

The first smart phone communicates (7016) through a network to a data repository for image processing and analysis of a first dataset including the first workflow and the first real-time translational and rotational trajectory (e.g., client devices 104-1 and 104-2 transmitting respective workflows and obtained translational and rotational trajectories to the data repository 108 through the network 106, FIG. 1). Furthermore, in some embodiments, the first dataset further includes the time-stamped coordinates of the feature points in the first workflow and the translational movement of the coordinates of the feature points in the first workflow. In some embodiments, the communicating occurs (7018) in real time concurrently with the obtaining and the acquiring. Additionally and/or alternatively, the first dataset is stored and communicated to the data repository at a time subsequent to the obtaining/acquiring of the dataset. In some embodiments, the first dataset is (7020) communicated wirelessly (e.g., using a wireless-protocol based communications interface 304, the client device 104 transmits the first dataset to the data repository 108 through the network 106, FIGS. 1 and 3). Alternatively, the first dataset is communicated over a wire (e.g., USB data transfer interface).

In some embodiments, additional datasets are communicated to the data repository for image processing and analysis. Additional datasets may include sets of data acquired by the same first smart phone that obtained and acquired data for the first dataset. This may, for example, correspond to scenarios in which the additional datasets representing the same subject are acquired by the same device, but at subsequent times (e.g., once every week) in order to assemble a temporal stack of spatial and spectral data for analysis, as in the case of detecting temporal observable changes. Alternatively, additional datasets may correspond to sets of data acquired by one or more additional smart phones distinct from the first smart phone, either concurrently with or at different times from the data capture session of the first smart phone. Whether the additional datasets are acquired by the same or different smart phones, different data capture sessions may be utilized, for example, to capture images of the first subject from different angles or ranges of angles (e.g., a first smart phone for capturing a first workflow of images from the front of the subject, and a second smart phone for capturing a second workflow of images from behind the subject). Different data capture sessions may also be utilized to capture images at different resolutions (e.g., a first dataset for capturing low-resolution images and a second dataset for capturing high-resolution images), and/or to capture image workflows representing distinct frequencies of light (or ranges of frequencies) (e.g., a first smart phone with an image sensor/pixilated detector configured to detect visible light frequencies, and a second smart phone with an image sensor/pixilated detector configured to detect IR light frequencies).

For example, referring now to FIG. 7B, the machine-to-machine network (e.g., surface informatics based detection system 100, FIG. 1) further includes a second smart phone (e.g., client device 104-1) in some embodiments. As described above, in other implementations, the second smart phone is the first smart phone, as opposed to a distinct smart phone. The second smart phone has a second two-dimensional pixilated detector (e.g., image/video capture module 332), at least one second accelerometer (e.g., sensor 326), at least one second gyroscope (e.g., sensor 326), one or more second processors (e.g., 302), and memory (e.g., memory 306, FIG. 3) for storing one or more programs for execution by the one or more second processors. The one or more programs include programs for real-time feature detection, real-time generation of feature-based coordinate point cloud systems, and/or active mapping and tracking of coordinate points of a point cloud system to image features. Optionally, the second smart phone includes one or more additional sensors (e.g., barometer, compass, light sensors, etc.) for acquiring additional sensor readings that may be used as additional mathematical variables in spatial, spectral, and/or temporal processing operations.

In some embodiments, the second smart phone obtains (7022) a respective time-stamped image of the first subject using the second two-dimensional pixilated detector at a third frequency, thereby obtaining a second workflow including a second plurality of time-stamped images. In some embodiments, the time-stamped images of the second workflow have coordinate mapped feature points for features of the first subject. In some embodiments, each time-stamped image of the second workflow has a respective time point of a third plurality of time points at which the respective time-stamped image was obtained.

In some embodiments, the second smart phone acquires (7024) a respective time-stamped accelerometer interval reading and a respective time-stamped gyroscope interval reading using the respective at least one second accelerometer and the at least one second gyroscope of the second smart phone at a fourth frequency independent of the third frequency. A second plurality of time-stamped accelerometer interval readings and a second plurality of time-stamped interval gyroscope readings is thereby obtained. In some embodiments, each of the time-stamped accelerometer interval readings and each of the time-stamped interval gyroscope readings have a respective time point of a fourth plurality of time points at which the respective reading was acquired. As a result, a second real-time translational and rotational trajectory of the second smart phone is thereby obtained which indicates a relative position of second smart phone with respect to the first subject through the second plurality of time-stamped images.

In some embodiments, the second smart phone acquires time-stamped coordinates of the feature points in the second workflow at each of the third plurality of time points, thereby obtaining real time translational movement of the coordinates of the feature points.

The second smart phone communicates (7026) through the network to the data repository for image processing and analysis of a second dataset including the second workflow and the second real-time translational and rotational trajectory. Furthermore, in some embodiments, the second dataset further includes the time-stamped coordinates of the feature points in the second workflow and the translational movement of the coordinates of the feature points in the second workflow.

In some embodiments, the first, second, third, and fourth plurality of time points are within a first timeframe corresponding to a first capture session (e.g., datasets from different smart phones, but from same capture session).

In some embodiments, the first and second plurality of time points are within a first timeframe corresponding to a first capture session, and the third and fourth plurality of time points are within a second timeframe corresponding to a second capture session, wherein the first timeframe predates the second timeframe. This corresponds to the scenario in which datasets are received from different smart phones, and from different capture sessions (e.g., in order to assemble a temporal stack of spatial and spectral data for analysis). In some further embodiments, the second computer-enabled imaging device is the first computer-enabled imaging device, the second two-dimensional pixilated detector is the first two-dimensional pixilated detector, the at least one second accelerometer is the at least one first accelerometer, the at least one second gyroscope is the at least one first gyroscope, the one or more second processors are the one or more first processors, and the memory for storing one or more programs for execution by the one or more second processors is the memory for storing one or more programs for execution by the one or more first processors. Here, datasets are received from the same smart phone, but from different capture sessions.

Operations 7022 through 7026 performed by the second smart phone may be performed in accordance with any of the embodiments described with respect to the first smart phone (e.g., operations 7002 through 7020). Furthermore, any of the operations 7002 through 7026 may be performed for any additional smart phones in order to produce additional, subsequent datasets for processing. As described above, the subsequent datasets may corresponds to data obtained and acquired by the same smart phone at different times, or data obtained and acquired by additional and distinct smart phones at the same or different times. An example in which multiple smart phones (e.g., client devices 104-1 through 104-4) are used for concurrent and varied data capture is illustrated in FIG. 4B.

Referring now to FIG. 7C, the data repository apparatus (e.g., data repository 108, FIGS. 1 and 2) receives the first dataset and/or second dataset (and/or additional subsequent datasets) from the first smart phone and/or the second smart phone (which, in some embodiments, is the first smart phone), and stores (7028) the first dataset and/or second dataset in a subject data store associated with the first subject in a first memory location in the computer memory (e.g., subject data store 214, FIG. 2). The various processing performed by the data repository apparatus using received datasets is described in greater detail below. Alternatively, in some embodiments, the first and/or second dataset are retained and processed on the first smart phone, where the various operations described with respect to the data repository apparatus below are performed by the first smart phone (e.g., constructing two or three-dimensional maps from datasets, creating dense point clouds for viewing, etc.).

In some embodiments, the data repository apparatus constructs (7030) a two or three-dimensional map from the first dataset and/or second dataset (and/or additional subsequent datasets). In some embodiments, constructing includes matching (7032) a two-dimensional feature in a first time-stamped image and a second time-stamped image in the first workflow and/or the second workflow. Two-dimensional features may include tight groups of high-contrast pixels identified in both the first and second time-stamped images. Next, a parallax is estimated (7034) between the first time-stamped image and the second time-stamped image using the first and/or the second real-time translational and rotational trajectory, and/or the translational movement of the coordinates of the feature points in the first and/or second workflow. When the estimated parallax satisfies a parallax threshold and the matched two-dimensional feature satisfies a matching threshold, a two or three-dimensional point is added (7036) to the two or three-dimensional map at a distance obtained by triangulating the first time-stamped image and the second time-stamped image using the first and/or second real-time translational and rotational trajectory. Referring now to FIG. 7D, the matching (7032), the estimating (7034), and the adding (7036) are repeated (7038) for a different first time-stamped image or a different second time-stamped image in the first workflow and/or the second workflow, or for a different two-dimensional feature. The two or three-dimensional map including a plurality of two or three-dimensional points is thereby constructed.

In some embodiments, each of the two or three-dimensional points represents (7040) an average value of a respective single pixel or a respective group of pixels across at least a subset of the first workflow and/or second workflow that were identified by the two or three-dimensional map as corresponding to each other. The average value may correspond to an average value of a color associated with each of the two or three-dimensional points (e.g., RGB color system, where red, green, and blue each have integer values ranging from 0 to 255). In some embodiments, each of the two or three-dimensional points includes (7042) a surface normal computed from translational and rotational values of the subset of the first workflow and/or second workflow.

In some embodiments, translational and rotational values from the first and/or second real-time translational and rotational trajectory of the first and/or second smart phone, for each time-stamped image in the first and/or second workflow (and/or additional subsequent workflows of subsequent datasets from the same or different smart phones), are used (7044) to refine the two or three-dimensional map constructed from the first dataset and/or second dataset (and/or subsequent datasets) (a process sometimes referred to as “bundle adjustment”). In some embodiments, “bundle adjustment” is an optimization that takes as inputs a set of two or three-dimensional points, a set of two or three-dimensional device poses (e.g., translational and/or rotational data for the smart phone), and/or any sensor measurements that relate points in the images to the devices (e.g., two-dimensional features observed in multiple images in the workflows) or that relate devices to devices (e.g., gyroscope and accelerometer readings). One or more algorithms (e.g., Levenberg-Marquardt algorithm) are used to find refined values for all the two or three-dimensional points and three-dimensional device poses which agree as well as possible with the sensor measurements. If additional sensor measurements ever become available, this bundle adjustment process can be performed again, taking the new measurements into account, in order to produce a refined map of the scene (i.e., updated three-dimensional points and three-dimensional device poses). For example, a second dataset may observe the same three-dimensional points as a first dataset, but from a new set of perspectives. Two-dimensional feature correspondences are first calculated between to the sets of images of the first and second dataset (see discussion of feature descriptors and matching, described in greater detail above), creating new measurements linking the new set of devices to the original set of three-dimensional points (and therefore also to the original devices).

In some embodiments, a first two or three-dimensional map is constructed from a first dataset, and a second two or three-dimensional map is constructed from a second dataset, where the first dataset corresponds to a time frame during which data was captured that predates the time frame of the second dataset. In other words, in some embodiments, a corresponding two or three-dimensional map is constructed for each received dataset. These embodiments enable the concurrent viewing of constructed maps corresponding to distinct data captures (e.g., datasets acquired at different times, datasets acquired with devices using distinct settings (e.g., one device configured for UV, another configured for IR), etc.). In other embodiments, a two or three-dimensional map is constructed from a first dataset, and a second dataset received at a later time frame is used in conjunction with the already constructed two or three-dimensional map (without constructing an additional two or three-dimensional map). Thus, the saved two or three-dimensional map (and correspondingly saved two-dimensional features) from a previous data collection is re-used (e.g., time-stamped images from second dataset used only to texture map the generated mesh).

Furthermore, in some embodiments, a dense point cloud representing the first subject is created (7046) from the two or three-dimensional map, the dense point cloud including a plurality of points. In an example in which a three-dimensional map is created, groups of pixels in a first time-stamped image may have corresponding pixels in a second time-stamped image, where the corresponding pixels represent the pixels in the first time-stamped image shifted over by a number of pixel positions, referred to as a pixel disparity. Using the obtained translational and rotational trajectory of the first smart phone, the pixel disparity of any group of pixels identified in multiple images can therefore be converted directly into a physical distance from the first smart phone, thus creating a dense three-dimensional point cloud that may include tens of thousands of points. In other embodiments, the dense point cloud is a two-dimensional map that includes a plurality of points (e.g., a mosaic that is a two-dimensional representation of a region of interest of the first subject). The active point cloud surface reference system thus expands at the edges as new areas of the subject are imaged, thereby extending the map and dense point cloud to include previously non-imaged structures.

Furthermore, not only do these embodiments allow for building and growing the extent of a two or three-dimensional surface, but they also allow for the addition of more detail as more resolution of an area is obtained. That is, the active point system adds new points (e.g., steps 7036) between existing points as more detail emerges (i.e., as a camera gets closer to the subject), thereby allowing for building multi-dimensional maps with increased detail as more pixels are obtained from imaging a structure or subject.

Consequently, the dense point cloud (or multiple dense point clouds) may be created in such a way that a resolution of a target area (e.g., observed features, feature points, region of the subject, etc.) is maintained or increased as a function of increasing proximity to the target area. In other words, the dense point cloud may be created such that “zooming” into a target area (e.g., increasing proximity to the target area while viewing a texture-mapped mesh generated in step 7050) does not decrease a viewing resolution of the target area, but rather maintains the same or increases the viewing resolution. By doing so, a subject's features, such as a human eye, may be displayed and viewed at closer distances without degrading a viewing resolution.

For example, in some embodiments, an additional data collection event is performed, where an additional dataset that includes time-stamped images of the first subject and corresponding sensor readings is obtained and communicated to the data repository (e.g., repeating steps 7002, 7008, and 7016 of the method 7000 for an additional dataset, FIG. 7A). Here, the time-stamped images and corresponding sensor readings are obtained at different positions along a pre-defined axis, where the pre-defined axis defines an axis along which the distance of the first smart phone from the subject varies. An example is shown in FIG. 4A, where positions along the y-axis correspond to distinct distances of the client device 104-1 from the user 102-1. Thus, referring to this example, obtaining the additional dataset includes capturing additional time-stamped images of the subject and acquiring corresponding sensor readings at distances incrementally closer to and/or farther from the subject. The two or three-dimensional map is then further constructed (step 7030) from the additional dataset, where the dense point cloud is created (step 7046) from the two or three-dimensional map. Thereafter, the dense point cloud (or any visualization subsequently generated from the point cloud, such as a texture-mapped mesh, step 7050) may be viewed and manipulated at any plurality of selected distances or scales (e.g., “zoom” levels) without decreasing a resolution of a target area being viewed.

In some embodiments, to induce additional parallax (e.g., for matching features in step 7032 and adding points the constructed map in step 7036), the time-stamped images and corresponding sensor readings are obtained at different positions along the pre-defined axis in accordance with a non-uniform capture pattern. For example, referring to FIG. 4A, the client device 104-1 may advance along the y-axis in a “zig-zag” fashion (i.e., from side to side) in order to induce extra parallax, thereby constructing a two or three-dimensional map/creating a dense point cloud having a higher resolution (i.e., greater number of points).

In some embodiments, the additional dataset is obtained after constructing the two or three-dimensional map (step 7030) or after creating the dense point cloud (step 7046). That is, in some embodiments, the additional dataset that includes time-stamped images of the subject and corresponding sensor readings is used to augment an existing map or dense point cloud such that a target area (e.g., observable features, selected region, etc.) of the subject may be viewed at fixed and/or increased resolutions. In some embodiments, the existing map and/or dense point cloud include at least of the same feature points across at least some of the plurality of distances (e.g., “zoom” levels).

Alternatively, in some embodiments, rather than creating a single dense point cloud that is based on the initial (i.e., the first and/or second) and additional datasets (i.e., which include images captured at various distances along a pre-defined axis defining a distance from the subject), multiple dense point clouds are created. More specifically, in some embodiments, one or more additional dense point clouds are created (e.g., by repeating step 7046 for each of one or more additional datasets), where each additional dense point cloud corresponds to a different distance (e.g., “zoom” level) of the first smart phone from the subject. In some embodiments, the initial map is constructed (step 7030) and/or the dense point cloud is created (step 7046) before creating the one or more additional dense point clouds. In creating each respective additional dense point cloud, respective time-stamped images of the subject and corresponding sensor readings are obtained (e.g., by orbiting the first subject) at a first position/distance along a pre-defined axis, where the pre-defined axis defines an axis along which the distance from the first smart phone and the subject varies (e.g., y-axis, FIG. 4A). The respective time-stamped images and sensor readings at the first distance are then used to build upon the initial map and/or initial dense point cloud to create the respective additional dense point cloud corresponding to the first position/distance along the pre-defined axis. This process is repeated for each additional distance (or “zoom level) of a plurality of distances, such that the one or more additional dense point clouds representing the subject at different distances are created. In some embodiments, the one or more additional dense point clouds are spatially aligned as a result of using the same initial map/dense point cloud as a base coordinate system upon which to create the additional dense point clouds.

Referring now to FIG. 7E, in some embodiments, the dense point cloud is then processed (7048) using a surface reconstruction algorithm to generate a mesh representing the first subject. Thus, by converting the dense point cloud and its constituent points (e.g., tens of thousands of sample points), a surface mesh of the first subject may be recreated. In some embodiments, a Poisson surface reconstruction algorithm is used to generate the mesh. In some embodiments, the mesh is a solid polygonal mesh (e.g., a set of connected triangular faces). The three-dimensional polygonal mesh is created for a number of reasons. One reason is that it allows a human user to visualize the high resolution image data from multiple angles by using a three-dimensional viewer (e.g., client application module 340, FIG. 3) that interactively projects any of the collected high resolution imagery directly onto the triangles of the polygonal mesh. Another reason is that, from a data processing and analysis perspective, the polygonal mesh enables the alignment of high resolution imagery and/or spectral data initially captured from different viewpoints. Because the spatial position of all cameras has been determined (e.g., the first real-time translational and rotational trajectory of the first smart phone), the otherwise non-aligned images can be projected onto the polygonal mesh such that each triangle of the mesh now contains aligned pixel data.

In some embodiments, after the mesh is created, a texture mapping algorithm is applied (7050) to the mesh to generate a texture-mapped mesh representing the first subject using one or more third time-stamped images of the first workflow and/or the second workflow. In some embodiments, the one or more third time-stamped images of the first workflow are high-resolution images (e.g., 3264×2448), whereas the first time-stamped image and the second time-stamped image in the first workflow (used for constructing the two or three-dimensional map) are low resolution images (e.g., 480×360). In some embodiments, projecting textures, imagery, or data onto a polygonal mesh includes the following operations for each triangle in the mesh: (1) the camera pose (e.g., (x,y,z), (yaw, pitch, roll), and focal length) associated with a high resolution image is used to project each of the three vertices of the given three-dimensional triangle into the image (each vertex lands on a specific two-dimensional pixel position in the image, thus specifying a two-dimensional triangular area of pixels in the original image, (2) for any given virtual three-dimensional camera, such as a user-chosen viewpoint in an interactive three-dimensional viewer application, the triangular image region from the original image (from step 1) is warped to occupy the on-screen projection of the same three-dimensional triangle.

In some embodiments, the dense point cloud, the mesh, and/or the texture-mapped mesh representing the first subject are stored (7052) in a second memory location of the data repository memory.

In some embodiments, two, three, or four-dimensional sets of data are extracted for processing in other systems, integration into other three dimensional virtual environments, and exportation to three dimensional printing or other three dimensional rendering processes. The sets of data may be extracted, for example, from the constructed map, dense point cloud, mesh, and/or texture-mapped mesh. In some embodiments, computed spectral and temporal relationships of two or three dimensional features of the subject are displayed (7054) on local (e.g., the first smart phone) or remotely networked devices (e.g., a client device 104-4, a dedicated display terminal), using the constructed map, dense point cloud, mesh, and/or texture-mapped mesh. Displaying spatial, spectral, and/or temporal relationships of two or three dimensional features of the subject data may include applying a variety of image processing techniques to the dense point cloud, the mesh, and/or the texture-mapped mesh. In some embodiments, a contour map is generated from the texture-mapped mesh. In some embodiments, a contour map includes data indicating the degree to which an observed lesion is raised above the skin. In some embodiments, the boundaries of surface observables are delineated (e.g., by identifying high contrast pixels) using data from a texture-mapped mesh. In one example, the boundary and shape of an observed lesion is traced and identified. In some embodiments, pigmentation maps are generated from a texture-mapped mesh by identifying varying degrees of color contrast. As an example, a skin blood map (e.g., showing blood pigmentation of a skin region) is generated from a texture-mapped mesh to indicate the progress of a healing wound. In some embodiments, displaying the spatial, spectral, and/or temporal relationships includes displaying the constructed map, dense point cloud, mesh, and/or texture-mapped mesh on a display device for manipulation. In some embodiments, the computed spectral and temporal relationships are displayed on virtual reality displays.

After creating the dense point cloud (and optionally generating the texture-mapped mesh), useful biological/non-biological information are extracted, processed, and analyzed in order to detect temporal observable changes, potential conditions, or pre-confirmed conditions for a subject in some embodiments. Analysis and processing are performed with respect to the spatial, spectral, and/or temporal aspects of extracted data.

The systems and methods described herein can be used in a variety of biological and non-biological applications.

In some embodiments, the described systems and methods are used to determine whether the subject has a wide variety of medical conditions, examples of which include, but are not limited to: abrasion, alopecia, atrophy, av malformation, battle sign, bullae, burrow, basal cell carcinoma, burn, candidal diaper dermatitis, cat-scratch disease, contact dermatitis, cutaneous larva migrans, cutis marmorata, dermatoma, ecchymosis, ephelides, erythema infectiosum, erythema multiforme, eschar, excoriation, fifth disease, folliculitis, graft vs. host disease, guttate, guttate psoriasis, hand, foot and mouth disease, Henoch-Schonlein purpura, herpes simplex, hives, id reaction, impetigo, insect bite, juvenile rheumatoid arthritis, Kawasaki disease, keloids, keratosis pilaris, Koebner phenomenon, Langerhans cell histiocytosis, leukemia, lichen striatus, lichenification, livedo reticularis, lymphangitis, measles, meningococcemia, molluscum contagiosum, neurofibromatosis, nevus, poison ivy dermatitis, psoriasis, scabies, scarlet fever, scar, seborrheic dermatitis, serum sickness, Shagreen plaque, Stevens-Johnson syndrome, strawberry tongue, swimmers' itch, telangiectasia, tinea capitis, tinea corporis, tuberous sclerosis, urticaria, varicella, varicella zoster, wheal, xanthoma, zosteriform, basal cell carcinoma, squamous cell carcinoma, malignant melanoma, dermatofibrosarcoma protuberans, Merkel cell carcinoma, and Kaposi's sarcoma. Additional examples are provided below.

Other examples include, but are not limited to, tissue viability (e.g., whether tissue is dead or living, and/or whether it is predicted to remain living); tissue ischemia; malignant cells or tissues (e.g., delineating malignant from benign tumors, dysplasias, precancerous tissue, metastasis); tissue infection and/or inflammation; and/or the presence of pathogens (e.g., bacterial or viral counts). Some embodiments include differentiating different types of tissue from each other, for example, differentiating bone from flesh, skin, and/or vasculature. Some embodiments exclude the characterization of vasculature.

The levels of certain chemicals in the body, which may or may not be naturally occurring in the body, can also be characterized. For example, chemicals reflective of blood flow, including oxyhemoglobin and deoxyhemoglobin, myoglobin, and deoxymyoglobin, cytochrome, pH, glucose, calcium, and any compounds that the subject may have ingested, such as illegal drugs, pharmaceutical compounds, or alcohol.

In some embodiments, the described systems and methods are used in a number of agricultural contexts and applications. Examples include general plant assessment, such as assessing plant types, plant height, green leaf material, number of leaves and general health status by assessing shape, greenness, and/or height of the plant. Other examples include plant status monitoring, which may include the use of multi-temporal images of the same plant material to assess plant growth rate and/or leaf area duration, and to accordingly adjust model-based yield estimations (e.g., collection of important morphological and physiological information for crops of plants over time to assess temporal features/parameters, such as growth rate, leaf area duration, etc.). Additional examples include species identification, whereby plant data of an unknown plant may be collected and compared against existing databases to identify a species of the plant (e.g., collecting morphological and physiological information of an unknown weed plant and comparing against a database of plant species to identify the plant species, provide treatment recommendations, determine if species is endemic to a certain habitat, etc.). Other examples also include disease or pest identification (e.g., capturing images of damaged surface features of crops and comparing against a database to determine presence of disease or infestation). Such systems and methods may be used in any other agricultural contexts or applications in which observable plant or crop features can be captured and analyzed.

Referring to FIG. 7F, in some embodiments, the data repository apparatus extracts (7056), from the dense point cloud (and/or the constructed map, mesh, or texture-mapped mesh) representing the first subject, values for observed features of the first subject for one or more observations for each of one or more first-subject observation sets. Each of the one or more first-subject observation sets corresponds to a respective class of features.

Classes of features may correspond to features of a biological or non-biological subject. Features of a class may be related physiologically, structurally, biologically, genetically, and/or in any other classifiable manner. Biological subjects include humans, plants, fungi, or other living organisms that are not plants, animals, or fungi. Human disease detection, for example, may include analyzing observation sets corresponding to classes of features such as skin features or eye features. In contrast, non-biological subjects may include physical structures or objects that are subject to change or defects (e.g., rusting of metal structures, automobile accidents, defective product on assembly line, etc.).

Different classes of features are pertinent to different surface informatics based detection scenarios. In some embodiments, surface informatics based detection or analysis in the context of vegetative target characteristics may involve classes of features for: plant identification (e.g., plant types, weed/invasive species detection, crop genotyping (seed registry)), plant phenotyping (e.g., height, leaf number, leaf morphology, plant count, plant morphology, root biomass, fruit count and size), plant biomass analysis (e.g., leaf area, leaf area index, plant biomass, crop yield), temporal plant analysis (e.g., plant growth rate, leaf area duration, crop yield prediction), plant physiology analysis (e.g., leaf pigments (chlorophyll, carotenoid, anthocyanin), plant water content, sugar and starch content in crops), and plant health detection (e.g., insect identification, disease identification and quantification, plant stress detection, water deficiency, treatment recommendation). In some embodiments, surface-informatics based detection or analysis in the context of human target characteristics involves classes of features for: genetic features (e.g., eye shape and color, nose shape and size, ear shape and size, lip size and shape, relative positioning of eyes, nose, mouth, ears, and hairline, head shape, skin color, hair color (dyed or natural)), aging features (e.g., solar damage (lentigines), wrinkles, graying, nose/ear size, head shape), and/or disease specific features (e.g., trauma wounds, crust, inflammation, induration, papules, nodules, ulceration, plaques, scale, blisters, bulla, vessel pattern, pigmentation, eye features, lesions). Other classes of features may also be used for animal identification, fungus identification, insect identification, aquatic plants and animals, non-biologic applications, terrain characteristics (e.g., soil type, rock type), construction (e.g., paint color, concrete, rust, corrosion, fatigue, fracture), urban landscape feature delineation, and/or rural landscape feature delineation.

Observations in an observation set are related groups of qualitative/quantitative data that correspond to a particular feature or characteristic of the corresponding class of features. As an example, an observation set corresponding to skin features may include a first observation corresponding to extracted data related to pigmentation (e.g., observed skin pigmentation of different surface regions of the subject) and a second observation corresponding to extracted data related to lesions (e.g., observed lesions of a subject). Feature data for an observation therefore includes quantitative and/or qualitative information for the particular observation, and may comprise various types of data related to the particular observation. Continuing the example above, feature data for the second observation corresponding to lesions may include data related to: location (e.g., diffuse, localized), lesion size and size distribution, percent body surface area, and lesion structures (e.g., scale, blood (deoxidation, oxidation, degradation), melanin (eumelanin, pheomelanin), collagen, other pigments (carotenoids, tattoo ink), structure uniformity (degree/extent of non-uniform features), and/or surface/subsurface features (milium cysts, comedomal openings, location of pigment)). As another example, for agricultural applications (as described above), feature data may include morphological and physiological information for a variety of related plant characteristics (e.g., plant size, number of leaves, pigmentation, etc.).

Extracting data may include reading data from subject datasets, and/or from the spatial, spectral, and/or temporal representations of subject datasets (e.g., constructed maps, dense point clouds, meshes, texture-mapped meshes, and/or any visualizations resulting from image processing, where the read data may be spatial, spectral, or temporal in nature). For example, data indicating the degree to which an observed lesion is raised above the skin may be read from a generated contour map. As another example, the size and shape of an observed lesion may be estimated using the boundary delineated by image processing performed on the texture-mapped mesh. In another example, the progress of a healing wound may be determined based on the changing pigmentation shown by a blood map generated from a texture-mapped mesh.

In some embodiments, each respective observation set in the one or more first-subject observation sets includes (7058) one or more observations. An individual observation of the one or more observations of a respective observation set includes: feature data (7060) of the individual observation of the respective observation set, and temporal data (7062) of the individual observation of the respective observation set. The temporal data describes a change in values for feature data for the individual observation over time. As described above, feature data may include quantitative and/or qualitative information for a particular observation (e.g., number, size, color of lesions). In contrast, temporal data represents observed changes in values of the feature data over a predefined period of time. For example, temporal data may indicate that since a last workflow capture, the number and size of previously observed lesions has increased by a measurable amount (e.g., expressed as a quantifiable amount, such as a quantity or percentage of change). Temporal data may represent changes in feature data measured with respect to any specified point or range of time (e.g., difference between a current value of feature data and a most-recently measured value, an initial value, a value measured on a certain date at a certain time, etc.).

As described below, various detection implementations may be used in the analysis of extracted data, where data extracted for a subject may be compared against stored data for other subjects/patients (FIGS. 7G-7H), or may itself be analyzed and compared against predefined thresholds (FIGS. 7I-7J).

Referring to FIG. 7G, in some embodiments, the data repository apparatus retrieves (7064) one or more stored observation sets, from the subject data store stored in the first memory location of the data repository memory, based on a correspondence between the class of features of the one or more first-subject observation sets and the class of features of the one or more stored observation sets. For example, if the feature data for an observation set corresponds to skin features as the associated class of features, the data repository apparatus accordingly retrieves stored observation sets corresponding to skin features. Stored observation sets may include feature data of and submitted by patients other than the first subject, where stored observation sets may be retrieved from a large-scale (e.g., worldwide) database configured to aggregate and manage a significant volume of records and patient data (e.g., data repository 108, FIGS. 1 and 2). As described above with respect to the first-subject observation sets, in some embodiments, each respective observation set in the one or more stored observation sets includes (7066) one or more observations. An individual observation of the one or more observations of a respective observation set includes: feature data (7068) of the individual observation of the respective observation set, and temporal data (7070) of the individual observation of the respective observation set. The temporal data describes a change in values for feature data for the individual observation over time.

After retrieving the one or more stored observation sets, a temporal observable change is detected (7072) for the first subject based on a numerical correlation with a corresponding pattern in at least one of the one or more stored observation sets. Additionally and/or alternatively, a potential condition (e.g., biological/non-biological, such as suspicious tissue growth, the spread of fractures in a building structure, etc.) or a pre-confirmed health condition is detected for the first subject based on a correlation with a corresponding pattern in at least one of the one or more stored observation sets.

Detection may occur in a variety of ways. In one example, data extracted for a subject includes data related to observed lesions (e.g., a first observation) on a subject's skin (e.g., a first observation set corresponding to skin features). Feature data may describe a number of characteristics of a particular lesion, including an observed size, shape, and color. Furthermore, the data for the subject collected over a predefined period of time (e.g., over three months) may indicate that the particular lesion has grown in size by over 50% (e.g., from 4 mm to in width to 6 mm), has become irregular in shape (e.g., exhibiting jagged edges and deviating from an initial circular shape), and has become darker in color (e.g., by a predefined number of shades). To determine if any pattern exhibited by the extracted data has been linked to confirmed health conditions, the extracted data (e.g., feature and/or temporal data) is correlated to, and subsequently compared against, the retrieved data of other patients and subjects for corresponding features. The resulting comparison may indicate, for example, that subjects who exhibit similar characteristics (e.g., similar size of an observed lesion, similar degree of change in the pigmentation of an observed lesion) have previously been diagnosed with certain conditions (e.g., pigmented basal cell carcinoma).

More specifically, in some embodiments, detecting (7072) a temporal observable change includes, for a respective first-subject observation set, correlating (7074) feature data and/or temporal data of at least a subset of the observations of the respective first-subject observation set with feature data and/or temporal data of at least a corresponding subset of the observations of a corresponding stored observation set. In other words, extracted data (e.g., feature and/or temporal data) for the first subject is compared against stored data for other subjects, where the compared data relates to the same observation within the same observation set. In some embodiments, correlating feature data may include a direct comparison of feature data (e.g., measured and noted characteristics for a particular observation, irrespective of temporal data) in the first-subject observation set and the corresponding stored observation set. In one example, the size of a lesion observed on the first subject is compared against the size of an observed lesion for another patient, as indicated in the stored observation set. In contrast, in some embodiments, correlating temporal data includes a direct comparison of temporal data (e.g., measured changes over a predefined period of time in feature data for a particular observation) in the first-subject observation set and the corresponding stored observation set. In one such example, the growth rate of a lesion for the first subject is compared against a lesion growth rate for another patient, as indicated in the stored observation set.

In some embodiments, correlating (7074) includes comparing the feature data for the first subject against an average value for the corresponding stored feature data. In one such example, the average value is an average percentage of growth based on all or a subset of the stored observation sets. In some embodiments, correlating (7074) includes comparing the feature data for the first subject against corresponding data for each of the plurality of stored observation sets, each stored observation set corresponding to feature and/or temporal data for a different subject (e.g., a different patient). In one such example, the stored observation sets includes respective data for a second, third, and fourth subject (e.g., different patients). Feature data (e.g., data for observed lesions) for the first subject is then compared to data for each of the second, third, and fourth subjects. The results of the comparison may indicate a disparity in data. For example, the comparison may indicate a +15% difference between data for the first and second subjects, where data for the first subject indicates a 50% increase in size, and data for the second subject (e.g., who has a confirmed diagnosis of basal cell carcinoma) indicates a 65% increase. Any known statistical techniques may then be applied for analyzing such results (e.g., calculating an average based on the three separate comparisons).

Based on the correlating, a respective numerical score is computed (7076) for each observation of the subset of observations of the respective first-subject observation set (e.g., a numerical score for skin pigmentation and a separate numerical score for lesions). In some embodiments, the respective numerical score is a factor of and is based on the numerical values calculated during the comparison. Continuing the example above, a numerical score based on feature data indicating a lesion growth rate that is 15% higher than the average lesion growth rate will be higher than a numerical score based on a lesion growth rate that is only 5% higher than the average. In accordance with a respective numerical score of the one or more numerical scores satisfying a corresponding feature score threshold, the temporal observable change (and/or pre-confirmed health condition) is detected (7078). In some embodiments, numerical scores for the subset of observations for the respective first-subject observation set are aggregated, and the aggregate numerical score for the respective first-subject observation set is compared against a corresponding feature score threshold.

In some embodiments, the subject data store is updated (7080) at the first memory location (e.g., of memory 206 of the data repository 108, FIG. 2) with one or more computed numerical scores. In some embodiments, the computed numerical scores that are stored have associated timestamps indicating the date/time at which the scores were computed, and are stored for later access on behalf of the first subject, or by other users with access to the data repository apparatus. In some embodiments, the subject data store is updated at the first memory location with an indication of the temporal observable change.

In some embodiments, in accordance with the respective numerical score of the one or more numerical scores satisfying the corresponding numerical score threshold, an alert is sent (7082) to a remote device associated with the first subject or a remote device associated with a caretaker of the first subject. The alert may include one or more forms of electronic communication (e.g., automated e-mail, text message, notification in proprietary application linked to the data repository apparatus, etc.).

In some embodiments, in accordance with the respective numerical score of the one or more numerical scores satisfying the corresponding numerical score threshold, one or more images are sent (7084) to a remote device associated with the first subject or a remote device associated with a caretaker of the first subject. The one or more images include at least a subset of the image data used by the correlating. For example, a photograph of an observed lesion (e.g., where lesions as an observation of an observation set have a numerical score exceeding a predefined threshold) is sent to a mobile device of the first subject.

In some embodiments, remote devices that receive alerts or images (in accordance with a numerical score satisfying a corresponding numerical score threshold) for the first subject are pre-authorized devices permitted to receive the alerts or images for the first subject (e.g., the first subject provides authorization for any remote devices and associated users to receive alerts/images).

In some embodiments, the data repository apparatus displays (or causes the display of) at least one time-stamped image of the first workflow and an indication of the temporal observable change on the at least one time-stamped image. In some embodiments, the data repository apparatus displays (or causes the display of) at least one time-stamped image of the first workflow and a false color display indication of a pixel or a pixel group that is associated with the temporal observable change.

Separate and apart from detection based on comparisons between extracted data for the first subject and the stored data of other subjects, observed changes in feature data over time that satisfy a predefined threshold may in and of themselves warrant attention.

Referring now to FIG. 7I, in some embodiments, a temporal observable change (additionally and/or alternatively, a potential biological/non-biological condition, or a pre-confirmed health condition) is detected (7086) for the first subject based on a variation in the one or more first-subject observation sets over time satisfying a temporal variation threshold. For example, detection systems (e.g., data repository 108) may be configured so that observed lesion growth rates in excess of 10% over the course of a month—although not linked to a pre-confirmed case of basal cell carcinoma—indicate detection of a temporal observable change. Variations in the one or more first-subject observation sets may be determined directly from the temporal data of observations in an observation set. As described above with respect to temporal data, variations may correspond to quantifiable or qualitative changes in any type of feature data (e.g., an increase in the number or size of previously observed lesions), and may be measured with respect to any specified point or range of time (e.g., difference between a current value of feature data and a most-recently measured value).

In some embodiments, each constituent type of feature data for a respective observation of an observation set has a corresponding temporal variation threshold. As an example, the change in an observed number lesions may have a first temporal variation threshold (e.g., +/−1 lesion), the change in size of observed lesions may have a second temporal variation threshold (e.g., +/−20% surface area), and the change in pigmentation of observed lesions may have a third temporal variation threshold (e.g., +/−2 shades). In some embodiments, a respective observation has a corresponding temporal variation threshold that applies to all constituent types of data (e.g., any variation of +/−20%, whether a change in the number, size, or pigmentation of observed lesions). In some embodiments, the temporal observable change is detected if a variation in any constituent type of feature data satisfies a temporal variation threshold (e.g., change in an observed number lesions satisfies threshold), while in other embodiments, the temporal observable change is detected if variations for a combination of constituent types of feature data satisfy their respective thresholds (e.g., both a change in the number and a pigmentation of an observed number lesions satisfy their respective thresholds).

In some embodiments, the indication of the temporal observable change is reported (7088). In some embodiments, reporting the indication includes (7090) updating the subject data store at the first memory location (e.g., of memory 206 of the data repository 108, FIG. 2) with the indication of the temporal observable change. In some embodiments, the indication includes an associated timestamp for the date/time at which the temporal observable change for the first subject was detected, the indication being stored for later access on behalf of the first subject, or by other users with access to the data repository apparatus. In some embodiments, reporting the indication includes (7092) displaying at least one time-stamped image of the workflow and an indication of the temporal observable change on the at least one time-stamped image. For example, a photograph of an observed lesion whose growth has exceeded a predefined temporal variation threshold, and a boundary delineation of the observed lesion, is displayed on a mobile device of the first subject. In some embodiments, multiple time-stamped images are displayed as a chronological series of images (e.g., or as a side-by-side comparison) such that the variation satisfying the temporal variation threshold is visually presented. In some embodiments, at least one time-stamped image of the workflow and a false color display indication of the pixel or the pixel group that is associated with the temporal observable change is displayed (7094) (e.g., an image is modified such that a lesion whose growth satisfies the temporal variation threshold is shown in a higher contrast color to surrounding regions).

Referring now to FIG. 7J, the detection of a temporal observable change, potential biological/non-biological condition, or known health condition may be detected based on pixel intensity across images of a workflow. That is, in some embodiments, the time-stamped images of the first workflow are aligned (7096) using the first real-time translational and rotational trajectory thereby creating an aligned workflow. Corresponding pixel intensities, or corresponding pixel group intensities, are then compared (7098) across the first aligned workflow, for satisfaction of an intensity variation threshold. When a pixel intensity or a pixel group intensity, across the first workflow, satisfies the intensity variation threshold, the pixel or the pixel group is reported. Pixel or pixel group intensities may correspond to features detected across the time-stamped images of a workflow, as indicated by high-contrast pixels or groups of pixels in comparison to surrounding pixels. As an example, a brown-colored lesion observed on skin having a pale complexion will have a higher color contrast. When the intensity (e.g., contrast, color intensity, etc.) of the pixels corresponding to the brown-colored lesion satisfy a threshold, the pixels are reported (e.g., to the first subject). Pixel or pixel group intensities may be expressed as a measure of brightness, contrast, or hue, as a specific color, and/or as a specific shape (or lack thereof). These pixel groups may be further combined and segregated into composite structures to identify and analyze underlying structural patterns (e.g., blood vessel shapes, extent and distribution of color variation in a pigmented lesion, location of scale at leading or trailing edge of pink lesion, extent of rust on a structure, damage on a product, etc.).

In some embodiments, reporting includes (7100) displaying at least one time-stamped image of the workflow and an indication of the pixel or the pixel group that satisfied the intensity variation threshold (e.g., displaying an image of the observed lesion whose corresponding pixels satisfied the intensity variation threshold). In some embodiments, reporting includes (7102) displaying at least one time-stamped image of the workflow and a false color display indication of the pixel or the pixel group that satisfied the intensity variation threshold (e.g., an image is modified such that a lesion whose pigmentation satisfies the intensity variation threshold is shown in a higher contrast color to surrounding regions). In some embodiments, reporting includes (7104) storing an indication of the pixel or the pixel group that, across the first workflow, satisfied the intensity variation threshold in the subject data store associated with the first subject in the first memory location in the computer memory (e.g., of memory 206 of the data repository 108, FIG. 2).

In some embodiments, the data repository apparatus retains an indexable file of observed features to allow for cross comparisons between spectral, spatial, and temporal characteristics of observed features on a plurality of subjects. In some embodiments, the indexable file is used to interrogate and annotate collated spectral, spatial, of temporal characteristics of observed features on a plurality of subjects. Furthermore, in some embodiments, the indexable file is used to correlate unique spectral, spatial, of temporal characteristics with specific biologic or non-biologic processes. In some embodiments, the file also includes a collection of composite structures, their structural patterns, temporal changes, and/or locations and size of observed features on subjects (biologic or non-biologic).

While some parts of the method 7000 in FIGS. 7A-7J are described with respect to the first smart phone and/or the second smart phone (e.g., first and/or second time-stamped images of a first and/or workflow), any of the embodiments described above may be analogously applied to each additional device of the machine-to-machine network.

Stages of method 7000 described with respect to FIGS. 7A-7J may be performed additionally and/or alternatively to one another. For example, temporal observable changes or pre-confirmed health conditions may be detected by way of comparison to stored observation sets, concurrently with determining whether variations in data for a first subject satisfying a temporal variation threshold (irrespective of being compared to stored observation sets).

For situations in which the systems discussed above collect information about users, the users may be provided with an opportunity to opt in/out of programs or features that may collect personal information (e.g., information about a user's preferences or a user's contributions to social content providers). In addition, in some embodiments, certain data may be anonymized in one or more ways before it is stored or used, so that personally identifiable information is removed. For example, a user's identity may be anonymized so that the personally identifiable information cannot be determined for or associated with the user, and so that user preferences or user interactions are generalized (for example, generalized based on user demographics) rather than associated with a particular user.

Although some of various drawings illustrate a number of logical stages in a particular order, stages which are not order dependent may be reordered and other stages may be combined or broken out. While some reordering or other groupings are specifically mentioned, others will be apparent to those of ordinary skill in the art, so the ordering and groupings presented herein are not an exhaustive list of alternatives. Moreover, it should be recognized that the stages could be implemented in hardware, firmware, software or any combination thereof.

The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the scope of the claims to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen in order to best explain the principles underlying the claims and their practical applications, to thereby enable others skilled in the art to best use the embodiments with various modifications as are suited to the particular uses contemplated.

Furthermore, in addition to the list of conditions described with respect to FIGS. 7F-7J, other medical conditions which the methods and systems described herein may be used to detect include, but are not limited to (categories are general and listings under one heading do not exclude a role in another heading): Acne (Vulgaris, Rosacea, Fulminans, neonatal, Pomade, Corticosteroid, Scars), Bites (Chiggers, Fleas, Bedbugs, Brown Recluse) and Stings (Bee, Wasp, Fire Ant, Scorpion, Jellyfish, Stingray), Dermatitis (Atopic, Contact, Contact Irritant, Blister Beetle, Caterpillar, Carpet Beetle, Coral, Sea Urchin, Sponges Seabather's Eruption, contact allergic (poison ivy, nickel, rubber)), Infections (impetigo, cellulitis, abscess, candidal intertrigo, Tinea (Barbae, Capitis, Corporis, Cruri, Faciei, Manuum, Pedis, nigra, Versicolor), Lyme disease, Rocky mountain spotted fever, Herpes simplex, Syphilis, molluscum contagiosum, human papilloma virus, Hand Foot mouth disease, measles, pseudomonas, Bacillary Angiomatosis, Anthrax, Condyloma Acuminatum, Cutaneous Larva Migrans, Erysipelas, Leishmaniasis, Leprosy, Meningococcemia, Myiasis, Tungiasis), Malignant Skin tumors (Melanoma (Acrolentiginous, Mucosal, Superficial Spreading, Lentigo Maligna, Desmoplastic, Nodular), Basal Cell Carcinoma (Morpheiform, Pigmented, Superficial), Squamous Cell Carcinoma, Bowen's Disease, Merkel Cell Carcinoma, Kaposi Sarcoma, Dermatofibroma Dermatofibrosarcoma Protuberans, Angosarcoma, Adnexal tumors, Cutaneous leukemia/lymphoma, Keratoacanthoma), Benign Skin tumors (Melanocytic Nevi (Atypical/Dysplastic, Becker's Blue Congenital Halo Intradermal Spillus Spitz Reed Ito/Ota Speckled Lentiginous), Lentigines (Actinic, Ink spot), Ephelides, Cafe au lait macule, Hemangiomas (Capillary, Cavernous), Neurofibroma, Angiokeratoma, Seborrheic Keratosis, Clear Cell Acanthoma, Nevus Sebaceous, Sebaceous Hyperplasia, Fibrous Papule, Keloid, Acrochordon), Systemic disease (Addison's Disease, Acromegaly, AIDS-Associated KS, Amyloidosis, jaundice, vitiligo, Porphyria, Porphyria Cutanea Tarda, Anemia, Antiphospholipid Syndrome, Neurofibromatosis, Behcet's Syndrome, Cryoglobulinaemia, Darier's Disease, Dermatitis Herpetiformis, Disseminated Intravascular Coagulation, Henoch-Schönlein Purpura, Hidradenitis Suppurativa, Hyperthyroidism, Xanthelasma, Xanthoma, Xanthogranuloma, Rheumatoid Arthritis, Gout, Psoriasis (guttate, vulgaris, Palmoplantar, Inverse, pustular)), Genetic disease (Albinism (tyrosinase negative/positive), Alkaptonuria (ochronosis), Accessory Tragus, Accessory Nipple, Anhidrotic Ectodermal Dysplasia Cowden Disease, Ehlers-Danlos Syndrome, Marfan's Syndrome, Geographic Tongue, LEOPARD Syndrome, Dysplastic Nevus Syndrome, Neurofibromatosis, Nevoid Basal Cell Carcinoma Syndrome, Ichthyosis (Vulgaris, Lamellar, X-Linked, Linearis Circumflexa), Osteogenesis Imperfecta, Peutz-Jeghers Syndrome, Steatocystoma multiplex, Waardenburg Synrome, Piebaldism, Xeroderma Pigmentosum, Eye Disease (Conjunctivitis, Cataracts, corneal abrasion, Blepharitis, stye, subconjunctival hemorrhage, Pterygium, exophthalmos, Oculodermal Melanocytosis hyphema, filariasis), Hair disorders (Alopecia (androgenic, areata, traumatic), Bubble Hair Deformity Hirsutism Hot comb alopecia, Pili Torti, Telogen Effluvium, Uncombable Hair Syndrome, Monilethrix, Menkes's Kinky Hair Syndrome, Nail disorders (Beau's Lines, Mees's Lines, Candidal Paronychia, Half and Half Nails, Leukonychia Ochronosis, Onychodystrophy, Onychogryphosis, Onycholysis, Onychomadesis, Onychomycosis, Onychorrhexis (Brittle Nails), Onychoschizia, Onychotillomania, Splinter hemorrhage, Terry's Nails, Yellow Nail Syndrome, Melanonychia, Subungual melanoma, Median Nail Dystrophy, Koilonychia (Spoon Nails), Aging (wrinkles, Angular Cheilitis, Asteatotic Dermatitis, Cellulite, Diabetic Dermopathy, Perléche, Rhinophyma, Senile/Actinic Purpura, Varicose Veins, Spider Veins, Telangiectases, Stasis Dermatitis, stasis ulcer, Pressure Ulcer), Light induced (Actinic Keratosis, Actinic Cheilitis, Actinic Reticuloid, Poikiloderma of Civatte, Photoallergic Contact Dermatitis, Phytophotodermatitis), Pediatric disorders (Acropustulosis of Infancy, Aplasia Cutis Congenita Congenital dermal melanosis (Mongolian spot) Diaper Dermatitis, Diffuse Neonatal Hemangiomatosis, Dyskeratosis Congenita, Epidermolysis Bullosa (EB, EBDD, EBDR, EBDD, EBS, Acquisita), Incontinentia Pigmenti, Netherton's Syndrome, Transient Bullous Dermolysis of the Newborn, Pachyonychia Congenita, Transient Neonatal Pustular Melanosis), Drug reactions (Atrophie Blanche, Cushing's Syndrome, Erythema Multiforme, Toxic Epidermal Necrolysis, stevens johnsons syndrome, Fixed Drug, urticarcia, vasculitis, angioedema), Oral Candidiasis (Thrush), Nutritional Deficiency (Vitamin C Deficiency—Scurvy, Zinc Deficiency, Kwashiorkor, or excess (obesity)) Toxins (Argyria, Arsenical Keratosis, Carotenemia, Choracne) Pigmentary Disorders (Leukoderma, Vitiligo, Post inflammatory hyperpigmentation or Hypopigmentation, Melasma), Immune/inflammatory disorders, Lupus (Erythematosus Bullous, Discoid, Subacute, Systemic) Linear IgA Bullous Dermatosis, Bullous Pemphigoid, Pemphigus (Foliaceus, Vegetans, Vulgaris, IgA, Paraneoplastic), Lichen (Aureus, Amyloidosis, Nitidus, Planus, Sclerosus et Atrophicus, Simplex Chronicus), Morphea, Scleroderma, Granuloma Annulare, Id Reaction, Livedo Reticularis Pityriasis (Alba, Rosea, Lichenoides, Rubra Pilaris), Pyoderma Gangrenosum, Pyogenic Granuloma, Sarcoidosis, Telangiectasis Macularis Eruptiva Perstans, Urticaria (Acute, Chronic, Dermographism, Solar, Vasculitis), Vasculitis-Leukocytoclastic, Median Rhomboid Glossitis), Necrobiosis Lipoidica (Necrobiosis Lipoidica Diabeticorum), Miliaria (Crystallina, Profunda, Rubra), Fox-Fordyce Disease, Keratosis Pilaris, Seborrheic Dermatitis, Burns (Chemical, Frostbite, Heat (First-Second Third Degree) Radiation Dermatitis, Erythema Ab Igne, Sunburn), and Trauma (Traumatic Purpura, Lymphedema, Friction blister, abrasion, laceration, Immersion Foot Syndromes, Tattoo). 

1. A computer-implemented method of employing surface informatics based detection using a first computer-enabled imaging device and a data processing and display system: at the first computer-enabled imaging device having a first two-dimensional pixilated detector, at least one first accelerometer, at least one first gyroscope, one or more first processors, and memory for storing one or more programs for execution by the one or more first processors, the one or more programs including programs for real-time feature detection, real-time generation of feature-based coordinate point cloud systems, and active mapping and tracking of coordinate points of a point cloud system to image features: obtaining a respective time-stamped image with coordinate-mapped feature points for features of a first subject in a plurality of subjects using the first two-dimensional pixilated detector at a first frequency, thereby obtaining a first workflow comprising a first plurality of time-stamped images, each time-stamped image of the first workflow having a respective time point of a first plurality of time points at which the respective time-stamped image was obtained; acquiring a respective time-stamped accelerometer interval reading and a respective time-stamped interval gyroscope reading using the respective at least one first accelerometer and the at least one first gyroscope at a second frequency independent of the first frequency, thereby acquiring a first plurality of time-stamped accelerometer interval readings and a first plurality of time-stamped interval gyroscope readings, each of the time-stamped accelerometer interval readings and each of the time-stamped interval gyroscope readings having a respective time point of a second plurality of time points at which the respective reading was acquired, thereby obtaining a first real-time translational and rotational trajectory of the first computer-enabled imaging device which indicates a relative position of the first computer-enabled imaging device with respect to the first subject through the first plurality of time-stamped images; and acquiring time-stamped coordinates of the feature points in the first workflow at each of the first plurality of time points, thereby obtaining real time translational movement of the coordinates of the feature points; and communicating, through a network to the data processing and display system for image processing and analysis, a first dataset comprising: the first workflow, the time-stamped coordinates of the feature points in the first workflow, the first real-time translational and rotational trajectory of the first computer-enabled imaging device, and the translational movement of the coordinates of the feature points in the first workflow, wherein the data processing and display system comprises one or more processors and memory for storing instructions for execution by the one or more processors, including instructions for storing the first dataset in a subject data store associated with the first subject in a first memory location in the computer memory.
 2. The computer-implemented method of claim 1, wherein the data processing and display system further comprises instructions, for execution by the one or more processors, for: A) constructing a two or three-dimensional map from the first dataset; B) using the time-stamped coordinates of the feature points, the translational movement of the coordinates of the feature points, and translational and rotational values from the first real-time translational and rotational trajectory of the first computer-enabled imaging device for each time-stamped image in the first workflow, to refine the two or three-dimensional map constructed from the first dataset; C) creating, from the two or three-dimensional map, a dense point cloud representing the first subject, the dense point cloud comprising a plurality of points; and D) storing, in a second memory location of the memory of the data processing and display system, the dense point cloud representing the first subject.
 3. The computer-implemented method of claim 2, wherein the instructions for constructing (A) comprise: (i) matching a two-dimensional feature in a first time-stamped image and a second time-stamped image in the first workflow; (ii) estimating a parallax between the first time-stamped image and the second time-stamped image using the first real-time translational and rotational trajectory and/or the translational movement of the coordinates of the feature points in the first workflow; (iii) adding, when the parallax between the first time-stamped image and the second time-stamped image satisfies a parallax threshold and the matched two-dimensional feature in the first time-stamped image and the second time-stamped image satisfies a matching threshold, a two or three-dimensional point to the two or three-dimensional map at a distance obtained by triangulating the first time-stamped image and the second time-stamped image using the first real-time translational and rotational trajectory; and (iv) repeating the matching (i), estimating (ii), and adding (iii) for a different first time-stamped image or a different second time-stamped image in the first workflow or a different two-dimensional feature, thereby constructing the two or three-dimensional map.
 4. The computer-implemented method of claim 2, wherein each point in the plurality of points of the dense point cloud: (i) represents an average value of a respective single pixel or a respective group of pixels across at least a subset of the first workflow that were identified by the two or three-dimensional map as corresponding to each other, and (ii) includes a surface normal computed from the translational and rotational values of the at least a subset of the first workflow.
 5. The computer-implemented method of any claim 2, wherein the data processing and display system further comprises instructions, for execution by the one or more processors, for: processing the dense point cloud using a surface reconstruction algorithm to generate a mesh representing the first subject; and applying a texture mapping algorithm to the mesh to generate a texture-mapped mesh representing the first subject using one or more additional time-stamped images of the first workflow.
 6. The computer-implemented method of claim 2, further comprising displaying computed spectral and temporal relationships of two or three dimensional features of the subject on local or remotely networked devices using the constructed map, dense point cloud, the mesh, and/or the texture-mapped mesh.
 7. The computer-implemented method of claim 2, wherein the displaying includes displaying the computed spectral and temporal relationships on virtual reality displays.
 8. The computer-implemented method of claim 1, wherein the machine-to-machine network further comprises a second computer-enabled imaging device, the method further comprising: at the second computer-enabled imaging device having a second two-dimensional pixilated detector, at least one second accelerometer, at least one second gyroscope, one or more second processors, and memory for storing one or more programs for execution by the one or more second processors, the one or more programs including programs for real-time feature detection, real-time generation of feature-based coordinate point cloud systems, and active mapping and tracking of coordinate points of a point cloud system to image features: obtaining a respective time-stamped image with coordinate-mapped feature points for features of the first subject using the second two-dimensional pixilated detector at a third frequency, thereby obtaining a second workflow comprising a second plurality of time-stamped images, each time-stamped image of the second workflow having a respective time point of a third plurality of time points at which the respective time-stamped image was obtained; acquiring a respective time-stamped accelerometer interval reading and a respective time-stamped interval gyroscope reading using the respective at least one second accelerometer and the at least one second gyroscope at a fourth frequency independent of the third frequency, thereby acquiring a second plurality of time-stamped accelerometer interval readings and a second plurality of time-stamped interval gyroscope readings, each of the time-stamped accelerometer interval readings and each of the time-stamped interval gyroscope readings having a respective time point of a fourth plurality of time points at which the respective reading was acquired, thereby obtaining a second real-time translational and rotational trajectory of the second computer-enabled imaging device which indicates a relative position of the second computer-enabled imaging device with respect to the first subject through the second plurality of time-stamped images; and acquiring time-stamped coordinates of the feature points in the second workflow at each of the third plurality of time points, thereby obtaining real time translational movement of the coordinates of the feature points; and communicating, through the network to the data processing and display system for image processing and analysis, the second dataset comprising: the second workflow, the time-stamped coordinates of the feature points in the second workflow, the second real-time translational and rotational trajectory of the second computer-enabled imaging device, and the translational movement of the coordinates of the feature points in the second workflow, at the data processing and display system, the instructions for constructing further comprise instructions for A) constructing the two or three-dimensional map from the first dataset and the second dataset, and the instructions further comprise instructions for B) using the time-stamped coordinates of the feature points in the first workflow and the second workflow, the translational movement of the coordinates of the feature points in the first workflow and the second workflow, translational and rotational values from the first real-time translational and rotational trajectory of the first computer-enabled imaging device for each time-stamped image in the first workflow, and translational and rotational values from the second real-time translational and rotational trajectory of the second computer-enabled imaging device for each time-stamped image in the second workflow, to refine the two or three-dimensional map constructed from the first dataset and the second dataset.
 9. The computer-implemented method of claim 8, wherein the instructions for (A) constructing the two or three-dimensional map from the first dataset and the second dataset comprise: (i) matching a two-dimensional feature in a third time-stamped image and a fourth time-stamped image selected from the first workflow and/or the second workflow; (ii) estimating a parallax between the third time-stamped image and the fourth time-stamped image using the first and/or second real-time translational and rotational trajectory, and/or the translational movement of the coordinates of the feature points in the first and/or second workflow; (iii) adding, when the parallax between the third time-stamped image and the fourth time-stamped image satisfies a parallax threshold and the matched two-dimensional feature in the third time-stamped image and the fourth time-stamped image satisfies a matching threshold, a two or three-dimensional point to the two or three-dimensional map at a distance obtained by triangulating the third time-stamped image and the fourth time-stamped image using the first and/or second real-time translational and rotational trajectory; and (iv) repeating the matching (i), estimating (ii), and adding (iii) for a different third time-stamped image or a different fourth time-stamped image in the first and/or second workflow, or a different two-dimensional feature, thereby constructing the two or three-dimensional map comprising a plurality of two or three-dimensional points.
 10. The computer-implemented method of claim 8, wherein each point in the plurality of points of the dense point cloud: (i) represents an average value of a respective single pixel or a respective group of pixels across at least a subset of the first and/or second workflow that were identified by the two or three-dimensional map as corresponding to each other, and (ii) includes a surface normal computed from the translational and rotational values of the at least a subset of the first and/or second workflow.
 11. The computer-implemented method of claim 8, wherein: the instructions for (A) constructing the two or three-dimensional map from the first dataset and the second dataset further comprise instructions for constructing the two or three-dimensional map from the first dataset, the second dataset, and subsequent datasets of the first subject, the subsequent datasets comprising subsequent workflows; and the instructions to (B) refine the two or three-dimensional map constructed from the first dataset and the second dataset further comprise instructions for using the time-stamped coordinates of the feature points in the first workflow and the second workflow, the translational movement of the coordinates of the feature points in the first workflow and the second workflow, translational and rotational values from the first real-time translational and rotational trajectory of the first computer-enabled imaging device for each time-stamped image in the first workflow, and translational and rotational values from the second real-time translational and rotational trajectory of the second computer-enabled imaging device for each time-stamped image in the second workflow, to refine the two or three-dimensional map constructed from the first dataset, the second dataset, and the subsequent datasets.
 12. The computer-implemented method of claim 8, wherein the first, second, third, and fourth plurality of time points are within a first timeframe corresponding to a first capture session.
 13. The computer-implemented method of claim 8, wherein the first and second plurality of time points are within a first timeframe corresponding to a first capture session, and the third and fourth plurality of time points are within a second timeframe corresponding to a second capture session, wherein the first timeframe predates the second timeframe.
 14. The computer-implemented method of claim 8, wherein: the second computer-enabled imaging device is the first computer-enabled imaging device, the second two-dimensional pixilated detector is the first two-dimensional pixilated detector, the at least one second accelerometer is the at least one first accelerometer, the at least one second gyroscope is the at least one first gyroscope, the one or more second processors are the one or more first processors, and the memory for storing one or more programs for execution by the one or more second processors is the memory for storing one or more programs for execution by the one or more first processors.
 15. The computer-implemented method of claim 1, wherein the communicating occurs in real time concurrently with the obtaining and the acquiring.
 16. The computer-implemented method of claim 1, wherein the first dataset is communicated wirelessly.
 17. The computer-implemented method of claim 1, wherein the first dataset is communicated over a wire.
 18. The computer-implemented method of claim 1, wherein the first dataset is retained and processed on the first computer-enabled imaging device.
 19. The computer-implemented method of claim 1, wherein the first frequency and second frequency are each independently between 10 Hz and 100 Hz.
 20. The computer-implemented method of claim 1, wherein the first frequency is 30 Hz and the second frequency is 100 Hz. 21-56. (canceled) 